{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Process Nodule Dataset\n",
    "\n",
    "## Summary\n",
    "\n",
    "* Load scans and convert to pixels\n",
    "* Process image by normalizing, then removing everything outside of the lung tissue\n",
    "* Generate nodule masks by loading nodule coordiates from list3.2 csv file and using the cellmagicwand tool over that coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "1d12eab6-3340-fa57-84a1-91fe13886996",
    "_uuid": "37d58725e31aa7cac17df51f9308d146ab39770f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#EDIT HERE##############################\n",
    "\n",
    "#File paths\n",
    "metadatapath=\"LIDC/LIDC-IDRI_MetaData.csv\"\n",
    "list32path=\"LIDC/list3.2.csv\"\n",
    "DOIfolderpath='LIDC/DOI/'\n",
    "datafolder='processeddata'\n",
    "\n",
    "########################################\n",
    "\n",
    "import cell_magic_wand as cmw\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import dicom\n",
    "import os\n",
    "import scipy.ndimage\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import time\n",
    "\n",
    "from skimage import measure, morphology\n",
    "#from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "from sklearn.cluster import KMeans\n",
    "from skimage.transform import resize\n",
    "from skimage.draw import circle\n",
    "#Load metadata\n",
    "meta=pd.read_csv(metadatapath)\n",
    "meta=meta.drop(meta[meta['Modality']!='CT'].index)\n",
    "meta=meta.reset_index()\n",
    "\n",
    "#Get folder names of CT data for each patient\n",
    "patients=[DOIfolderpath+meta['Patient Id'][i] for i in range(len(meta))]\n",
    "datfolder=[]\n",
    "for i in range(0,len(meta)-1):\n",
    "    for path in os.listdir(patients[i]):\n",
    "        if os.path.exists(patients[i]+'/'+path+'/'+meta['Series UID'][i]):\n",
    "            datfolder.append(patients[i]+'/'+path+'/'+meta['Series UID'][i])\n",
    "patients=datfolder\n",
    "\n",
    "#Load nodules locations\n",
    "nodulelocations=pd.read_csv(list32path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "4f50e5b1-c1e8-14a8-591b-eb466e5adc0d",
    "_uuid": "6349e54cf8bf403404c2f71e34f6b7f0ed0341ba",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the scans in given folder path\n",
    "# code sourced from https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial\n",
    "def load_scan(path):\n",
    "    slices = [dicom.read_file(path + '/' + s, force=True) for s in os.listdir(path) if s.endswith('.dcm')]\n",
    "    slices.sort(key = lambda x: float(x.ImagePositionPatient[2]), reverse=True)\n",
    "    try:\n",
    "        slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])\n",
    "    except:\n",
    "        slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)\n",
    "        \n",
    "    for s in slices:\n",
    "        s.SliceThickness = slice_thickness\n",
    "        \n",
    "    return slices\n",
    "\n",
    "#convert to ndarray\n",
    "def get_pixels_hu(slices):\n",
    "    image = np.stack([s.pixel_array for s in slices])\n",
    "    # Convert to int16 (from sometimes int16), \n",
    "    # should be possible as values should always be low enough (<32k)\n",
    "    image = image.astype(np.int16)\n",
    "\n",
    "    # Set outside-of-scan pixels to 0\n",
    "    # The intercept is usually -1024, so air is approximately 0\n",
    "    image[image == -2000] = 0\n",
    "    \n",
    "    # Convert to Hounsfield units (HU)\n",
    "    for slice_number in range(len(slices)):\n",
    "        \n",
    "        intercept = slices[slice_number].RescaleIntercept\n",
    "        slope = slices[slice_number].RescaleSlope\n",
    "        \n",
    "        if slope != 1:\n",
    "            image[slice_number] = slope * image[slice_number].astype(np.float64)\n",
    "            image[slice_number] = image[slice_number].astype(np.int16)\n",
    "            \n",
    "        image[slice_number] += np.int16(intercept)\n",
    "    \n",
    "    return np.array(image, dtype=np.int16)\n",
    "\n",
    "def largest_label_volume(im, bg=-1):\n",
    "    vals, counts = np.unique(im, return_counts=True)\n",
    "\n",
    "    counts = counts[vals != bg]\n",
    "    vals = vals[vals != bg]\n",
    "\n",
    "    if len(counts) > 0:\n",
    "        return vals[np.argmax(counts)]\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "def segment_lung_mask(image, fill_lung_structures=True, dilate=False):\n",
    "    \n",
    "    # not actually binary, but 1 and 2. \n",
    "    # 0 is treated as background, which we do not want\n",
    "    binary_image = np.array(image > -320, dtype=np.int8)+1\n",
    "    labels = measure.label(binary_image)\n",
    "    \n",
    "    # Pick the pixel in the very corner to determine which label is air.\n",
    "    #   Improvement: Pick multiple background labels from around the patient\n",
    "    #   More resistant to \"trays\" on which the patient lays cutting the air \n",
    "    #   around the person in half\n",
    "    background_label = labels[0,0,0]\n",
    "    \n",
    "    #Fill the air around the person\n",
    "    binary_image[background_label == labels] = 2\n",
    "    \n",
    "    # Method of filling the lung structures (that is superior to something like \n",
    "    # morphological closing)\n",
    "    if fill_lung_structures==True:\n",
    "        # For every slice we determine the largest solid structure\n",
    "        for i, axial_slice in enumerate(binary_image):\n",
    "            axial_slice = axial_slice - 1\n",
    "            labeling = measure.label(axial_slice)\n",
    "            l_max = largest_label_volume(labeling, bg=0)\n",
    "            \n",
    "            if l_max is not None: #This slice contains some lung\n",
    "                binary_image[i][labeling != l_max] = 1\n",
    "\n",
    "    \n",
    "    binary_image -= 1 #Make the image actual binary\n",
    "    binary_image = 1-binary_image # Invert it, lungs are now 1\n",
    "    \n",
    "    # Remove other air pockets insided body\n",
    "    labels = measure.label(binary_image, background=0)\n",
    "    l_max = largest_label_volume(labels, bg=0)\n",
    "    if l_max is not None: # There are air pockets\n",
    "        binary_image[labels != l_max] = 0\n",
    "    \n",
    "    if dilate==True:\n",
    "        for i in range(binary_image.shape[0]):\n",
    "            binary_image[i]=morphology.dilation(binary_image[i],np.ones([10,10]))\n",
    "    return binary_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "b0012bd3-50f6-278b-de58-a2a39361f1bb",
    "_uuid": "c92e2b857adffd764cbb0fd5cdf67e5c5baaa023"
   },
   "outputs": [
    {
     "data": {
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1wLYc76g6V8+IRkhE/G1EHMuPD1P8fRIUjw9aFxFHI+JpoAeYJ2kacEZEPBzF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\nIjMzq5QTkZmZVcqJyMzMKvX/AQ2qEwBx2x9KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd55a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gm4MGxy+LUVGpnZxQ3rtAb72lk4TR67uUB7glHWlgayZqt2G3B3DDCB0JGDld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\nSL0E9LSnyqEJWUdGOXCcSlC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CkMyG4r0PNqEr5uM9Or4wSa5D6hyYIPWhSk8HTtbmFAnZ\nrDit3sz95Pfd0OcBiYdicsIZODxD9fUDhVDiFPdSqxYKUNu8bgNXaC8sdXkrYPZ+Pu4ne50CT+1E\nPsHQZcqFvBLPsvi9IwCUAxMyyEsdOf13rMFLF3WnUfUCBwPAVxmy7hhnjGYhEL++FTF/q8HsUczX\nOUAwcOh0dZyzL+j5RKNUUmgu2SQpjgWpX/VllSKrowaPPtFicbdJz8g2GYwtsOSo8CsZZfLWCFAW\n8h/GBlZBV4d9qffzFNuConRCJfaithAg98G2e3Ajale3R2m9RE6NVu9JEXo29SEbW/Pvvo88Sygm\nZXqn08dlBi3zPiCPufoaX2pBVrPbsclbuyk3Nyi7H/06ieoaW3HrltUjkKVIvEi5kmslavrKrew4\nNOpGc504Ru2sSrUb1NTPqxFuyXBYpa3cG6A9IRz9YRQ6rIN9QoUO7Lc/3+zLqNewWtnpWL43qzik\n0pwZApDa56UlOZcVS5DSycsifU8+OsELv7MRG4ZGPAZJ9qEbxHgJF3VH70iFC3egjjlmldsx/n6K\nsa0SFXnwdjPC9HFuA1d7OFoOAjXgJvYRm4Dl7QabQzl247Xe4iC4IXAviWd14RGn5La6jHxyohmW\ntaFUSvsEvPUCN3sUBqaPIppVKKJe/Q5keh0xAM0PqfaMOg2AP9cN57rvbZwis4Zuv0G7EDDUwCfP\nqJR5+rHDLvfGRcvVAoaa0gZY7n1v9Kl9uipJPUXX3/wGMVavVzOQJ44O6G5ftlDjIMbH/e8Qpid9\npmhBUFkppoYrK+hMTmNphARk63p3ThERlxiFDpYCuNTdSek+vei+ixdadPvisYhTSiHbEnLsowQp\niiFreZcTbRYaXeeuMCdFrX5VAGYMzeck8EDtjZyJOsdpYgSJsmdPCpzBlWzCx1bcnN2e1DN/wJge\n96ZaGctoxdCq+1GqUVe2vct+fQ2esn4tDKdUPKssfxcVTfNLNgtGPHSqrE5sZaYeXL2QcXYXDwJG\n96v5WtscvNDb7BPahfY3yy7gxYRPn51wsTSFlyhXBxh0YNV7H0AGjk+1PaCwY7SXgBhgoDAwWnL5\nknyJrfio+xS/cPgdSX6RQ5zlPGEMyaWY6tKBReBiMEa4XYOs8e6j85EXkkaTd6Skplrn3S93WNxp\nLBkLIIwhbAghRqs/ToDlPUZ/o8fkvQbNKjEL5EELcjjpQaFqYwHcVdtNhUguPVvTlJhMNyc0ywys\nYjtJ7yEyeAJwQieKjCYlSN1/sxOdOlDOsORWY3ILWz0ZNoywEpedsidNZz9qbzJvQV7JGNYp/X2T\nLmAJf9+oURp5opOeUgkqZQ2xGTKMbXEMhV2qOocisDkgzB5SwVKCu7exDmfTehIbw9UBBqWYugkH\npQmaLP42iOpOY4jvP02qwg3Gie7VwOHRWl+uG9hhA8RW3JSz90kWLiEDlWcfuvxXAUPzKuSIRscZ\nUb2sFAcByAAvgnmQgnOSdLMkJen6sGHM349pjwGJaWhPe0uPtrrZ4PiVHq/8C6/jn7v5Fn7zzU/g\nweEB2q/P0SwJ/bwema6fzhpQlCWRtk+TupjXIzUy6KZbDKwPCZPTvMGueaBSvwe3ziNOCJPjKEvI\nNSGwn9iU+z2sZE2FSHg2+4K8L5307h0pUE9l01gmWNJe9eBayHrq+3bBWB8BoSNwYIQVGcDZGKje\nrV+Ja3W3yZmS3N+le3H43zOS9U3C+nHALKYU+lNJStOsRyiBsjUe/rRruTLGRzO+6byi0oVVqw92\n3HVCHarrX5i31mop2ILWHyEBTRFoF4TZe+MJM+x+qgak9kidbMc085CtY/BtT4ZD4rxMerCM3NFT\n306JFpRr4yQgToKESLNsfzY97tGcBjxazvFbb30cJ8spJtMO/Q8sMH0IhHXuIwVPbnOCkjNf1cZJ\nKIJt2BsbDTySRVrmyw8SBbnZ05kCi97UiE3/LiVeIwVspXen8R/dXsDmsLH9SwEf7DXS1ujeUXrf\n3X6QtriUabURUQUOGGhXjH4vvb9ecjUAyPaK9EzeC1HT+MLrFar+20W6M7C6Jc+gkaJ13TVzeZJy\nJYCBb+xDdSc7ltJUmfvKWfmtOOlWvJAzoibGaJzpjZwPNAtCWANTt1OzuZwUzV0eQW2P3x+g8DHr\ncwSlm2lQOjpr1dTLaNVtaXXpxi7uPNWxNYfBNKDfi2hCxKOTOZomYvVohvu3H6OfAZMTQnsiFcaJ\nTGzfR36wsvNkUC9ZjrzbjRu2BUMKBMYeNMCoFTqcoxTTKsreGcdCWhfhKLSlTg+w1ZrNyu1T6l2d\ncJNDQca9L7BksFrcDbIS1aVlh3f3Vmn5w4YRp2xjpN/jPBbr/64UUt8LqYpJDAy/rnhVwbJx6aZL\nKYzcuyb9mLOFdpT2gL1AuRLAoEUt63FCxeTtp8ESaIwV1ZWpcyjtJnHo84KYAtUrlFeK2iwkh+Tk\nmPLQfhIAACAASURBVDE58XqDtKFYEMX5v4ASD150LUUK+4kHpFSnT5ia94LM9wgpxZlKtNjK1m0S\nxCOTaLNP+OM//iXc2z/BejHBZz/x+7hx7wTMhFt//A0sPhIROoC6pFYkaVhb0wQAZbKGtXhowjrd\nt3GLqhxwGrAkBlesFVCjJSGvB5ko8GXPDjtPga2KTNerTac2+lnfKbhEJDdnEHaSjLMHb/SYHKeM\nWFp3CmHXvVH9YjcJuOL0GanNGLArP9HFdZzWU+h3NzYLb1glrDwoe4YZG+D0biMZuibBhFA9X2y4\n9jzYy3TXcnVsDBDajZTBxtOw1a3GOlfLmOS3Dgcy29DzR6IcAVdHurbZ5IE8OUWWXnpNAgfxyRNY\nDSIoEb+mdTlunky/tgxNSV82dQruPl4CJhWln5G4TtNx3aZMVa+wjqCjBq+e3MKrD26B+4DvLG7i\nX3rhddybHePBeh/rH2yx+YN72Bwy5m8H9DNGnEnD82CkHB8RJcVbk1SQmDJBxdbp585dPKC3ATnY\nyvW5MaYmh283m/w+TOLtBfMG1SwxdzSZ2zC7EBntqSI10PRqjHQAFIAYSHR2tRF51yaAyXsNurmk\newsrEpYEl3LO2FV+X0WYOZXnDHYTQ3VudUyHQz8XO1xI29kFnfy2RiIDpV4z2lfnlCsDDP0sIM7I\nItoYMlCWtxssXqBhJ+tX/8AMBDUKqXR3HVWXgi0ABRiJjSHmAa/3dq5PNRICjh7qrlHOJ69JTZGs\n5ACMq3GaeOafbqgwRBoDSX56tZ+oZFVQiSH72tc3Wjz+RMA8BnzqhbeBF4BvPLyDn37ln2DDDb5v\nDvw7934HP7/4c5j/4xsCiGtCPIWFhNtAVibUiVHWS8w4KW0SgyxVAIgYxNpHVA569y5Vr8/p8GEW\n+PVhg8X9gP03ewMW3TODvOGwKSecGkepy33n97gkV1c/E4OnTwKj75uJcPAdwsNPRbRLiUfpp0Cz\n0HeewdMv++f0jCEmt3mWIbl/qjFY7KGpdTgm0u1JvlNZ5KVJk4UZFTufaRWMYf7IHcrVAIYgDxs6\nRrvo0e01WN0MkpRlv5JGI1K/gEZgoHMV51fn+lIb/ULhNkyHOX+vQSnn+SegRemmMmDL1nFu8oIq\nIEgqdIJlBbLn9glPkSm4z0Ugg1Okx/RRj/m7Ad934wFOuiluT08RwPhje1/DN7q7+Pb6Ln5o+iZW\nb+xjvkn5Jip1xz+30uK8K1aS/hUdLvrQi6p0fVN5HoCsFnED9BNCw/IcUpkIDA1ua5dpHUgQQNVJ\nrbcpciAwi08/ukmuKoxlQJLP1DP6WZPcveVzcAQIEssQ1oTY5rYNimOx7I4xHGiMMNmCZenvTph5\ntyVPpN05diZnCQOcGqsGzshA662ku5UrAQxMshszdRGbGw1OXmjQHZQ78qi/3fRYYNCBUldGVw7D\n7qiBxaSGBqNovdvoKsrjtbegXrSihkOlwkbzkrQPGwazhHyf3pPX0awlWQhFWYptUZW6TwJS+xli\nbJwSVkdqmEtiiYEb7RI/efuf4W++9kdxb36CX370R/DVkxfwib0H2HBAcxKyr33kGdWQWzMuH5o+\nUJl8f1XA6Qd+sWiK00GSdxbTQA+rXgyWE6BRUABKAHCAZvp6dHWnBWl2elIzNPpSWczkOGJ5u5Ec\nnsqUOIdXzx5FTI4b87pYLoZKSHkaD99tVIEDyhMKZjs29pxwZBfTY5IqGW7HWIP8fjFd4koAAwDo\nFuAPP+l2M+IMCPp9IKWLSpxkr5iBt0kUlA8oB3A10TPdzNcWaeL9C002ANuUlLJkbJbRBpy64+wZ\n18Bs3WP2Powix4nQ29gGk7RhlZ9FDa4gxvJ2g0ffD1AMuPe7EZNTRrtk/KUXfh0/9+1/G3/ho/8U\nU+rxqemb+BX8y/jk7B38t2//65i9R0a/m2Q38MuFQ1cCqQKz7OE5hJIaJAE5T1UJY0ea10HDsxM4\nNKuk/7OEMq9uS5KZ0AHT96MFLeXKXXyHxSxw4dHy61DCpoxXKKQ7SzLdzWFAu0jBbEBewblk7L3F\nWN3SRW35WQf2rjGwZJXgI+Dg+68WWvVxFtfv/EGfVCXK5KwlRBJXZgFUaZ+Wi5QrAQyyCIixuit7\nIxQUyg8ychRpRCXYZofIFSDTyQo0BnYLkhj/9rQEGbFfkAVVFS+xSlkepynWoMo/4PW9gZ2jz5uq\naOo2kQKOMlch3u2S0Z4EaMq0fhbQzQknMeBP3v0i/vnZ6/jx2QTAFK/tfwM/MHkXf/nX/ywOWSYn\na/xExADsCobm2mzsDePvIp+c+129MZqyLLZAk/aC8KrS5FiY4/K2LJxrH7Etre5nIhU1oMn6BK5f\nbaak+zvhElw4trko049yPVnwk73vVFezEjan2xV6IaJAs41hbjN4j6nGY+zBvwPdjk4mfTZEqru4\nNoILoFxMlbga7koG4ixgeVuaY64op8d7o84AELZMcm9EG4AJ5z9PlbMR0a3TB/Jqx4g8KWn4EszV\nyMjZl/R3HfyhvMa73kyNIVEjwpphlDhdp+692BJO77VY3gmYPgLak5RnsSX0c8K/9cv/BT5/8I0E\nClL+zMEp/sz/+Z9g9laLbi+vfPRG1qCBT+k57b8HBN+vtbT0z+JKnACro4BuL9iiMjU0+knQzwOW\ntwibQ6S9MiKalYBBs5J9PDR+watnBctL//MGPuU7go+RQPrMyO5px1KlD9iMr0UQWiyff+BGPaMU\nbKwq5u6t+pg4A4PdN5BsUzBiE9OFcs+n8RHA8lYjW9i7LeWLtGKJgvkHHiAs4CQT8uTXwq4OJ8nM\n8s55ojTrtDeCGoe8lE8bgtTSW4/niUwpxJrBLmU8E4EClwt7oGBYGhqVBpNJVti6jfd/cIrjT8pD\ntieE6SNgdZOwvkFYHwHTh4Q/+gt/Cav7Pf7wz/13AIA//ft/Cvv/7x7UrqITU0N1Cx23cQPL9WHR\nn2MSUo/FfL3e78GP5BOaFeH2lxmzR9GMj5t98cuvb4racPidHs0yJhefNM73q6pwhLysXdUsv0uV\nLIlHmavTMSSz3DPMFZzdzJp7Q5ibtxV4b4RXNwfehhHVgAk5rgP5WgPfEZBRwLCxQcm2sBaw5GlA\nnIb0PZ93HkjV5WoAA8k+BH6gGYPynZkGrfcwDOLU2R2vO8MzB38TvS79Fics7ruZWMXbRbRIQ44Q\nvVPbM+a6JDmHuggmGS1xIkFIYmR1LsiR1FzESIvGEtVNejOBi8GyupMHXNjIrliLe8EmvOy7ydh7\ntcErv/If4/D+CdZfvIlmDnMFUgQmx2K03ByQgUGhB/uBWwGZxYeMATEB1OW4iPVNtpdHUeIgHr0S\ncO/3JHX+5oCwOSDxRPVAewrZDDfp0RqXoBsADd6hfnd2IDvsQIKTC5Kwxb2nzEhdjBoj0Lj+acox\nKScgT2rXVzVA2LkowcQbvb23p1bvPDOIUwmFDx3bPhqSIJhgm/peEBSAKwIMTEC3v0U5g6OuwLiU\n0pejL6qWwqOVuvORX1A/Y1BP6KfyWzeX5a6aeVn/mPJqyfyy/d6VAE8C4pSwOGoRW7FsS8aldL+Y\ndUWzMFcJXXyGZX/u5qi11XpihAPmD2TEHH80yJ6UrTzeesqYvzbB+q2baE/Eyq8rAw++w5ik5LZh\nE7C8W96/HpgDKajMrZ4ggHk1mABOe2XmB0v9e8jYHAgo9DNCvycqR9MDB29Vse3OBan2FDNQA5mB\naT+6cWOh6o4B+P4kDTCrx0bGMhkTykhHAEWfvXAhO0bghdnAPjMm1Z3gG9gcUunnsoDMZ4pm5Pd1\n2XI1gCFtiGK5EMcQFhU7qCa1z6qj/wcDFvl8k7we4SOwuRUxeS/Irk1zYLEKtg2dSQRHLX0WXgs6\nSufGacBmP6CfAftv92YV95I4q0b6MHITcmhfU1AwMDnucecrjMcvN1gfyTN1ewHzB72oErZhoHgz\n2gUQN+KnbxdAe8q48e28pBmRMTkJ2BxO0O1llW4g0aB9MPIcHjjSJOMW4A6Wqbp8oQCi5DXs5pLe\nXp91/w0xQppaCAXnJAlTfIH2vyadFWMvZbrtwYKzQRiAJZ3V8wZF+z3VtTkU0Az9yLn+Ghr57H4v\nJq0KEd+P9f090yVYSHS3HxDWEvuj/Uk9A7YnSbU14gXKlQCGqMk9a+ql6Kr+25r662k6aStQGPNq\nmM48wjy8zt0sCau7EWHtfMae6qVos0GeB8Ai68KGMX+vx+xhumdlPxBX2oiB0kurmCeD6dHpucKa\ncfTtDrEhLO8ELO4EHH4nYnLMCKsgLkUGJsdpQHfKLBh773RmodfJ0Szl2n6eKfOZnh4nYUdZXNAs\n2+QCthiUmBIiENaE1e00UVsAKRx6fZNw0rWYPao2De7yAipA9OvNQbAELQAXfWSeC017lhKwsE4i\nrbfKtWHtTe8iTqnMn4mhqlCPN/NWOPe4v8aDAzEkf0gl9Pw18hwSKNallarNsmZVuDQY+HIlgMGQ\nsupURdPiHJSD0OtsdXxCbfAxnbjqdK0jNrAkKWED8H6P5SxmqzmXNFQNauaOVDYR0yT21DG5Of0A\nrFNz6XHVfzUcWupJaodu8Jru0zcS57D3TrT1+e0yyOasQULMdX2DJJUBZo96M7DZJEtej9mjKGtT\nSK6xCMeLDDYFyPS1O5SowWaJImlK2CAv9a6o9fpIJPTJpkHYSIKZyWOJNQid2myA45cDiIG9NyXo\nqFlIkFmxCe7Y3grpPmN0Oy90y+d181AYN4v3RluOp88eFAqBVbGHkN7RqBrgQCd04u6dnMThvIEK\nQMm0TZtYgMyu5WoAw0jRMNnaXVPoZlqcSrFNH9523H5L/9uTYFu+06zHzZunWN+4hemj3lba6b30\nvt1eMOrvszlZ8wrwcAt9XB228Ux6qf00oIkRlAa5AZyul+gYCEC7lBcfIPt6Lu5PrH+oF7Vh9kAu\n7vZlX4z2NGY3abLWIwHe5KTH3ruUw5SnhH4mS6a1r/KIr95FUg1MWqePTJDdvCpmF6f5XJsrAZg+\nkvgRQPT6fib/NweEU2oyM4zA/huM+UPJsMUNyX6WFAw4baWll7pe3akAop9JqntZ3wIzAK9uugdC\nvr/ZWbxdyL3/wXgjFIKoUHmRBeSYvUH7PGw471VaFwMeYZmXLVcWGLx1doyuW/GDs0Jgqr6DhK55\nP3QBHr2oEPEoSZxVgy5KrP40bcrncw6qdA+dRB+qgbE9idmNmWho4Exh8/3SwNVNUfV5GJbpl9uQ\n26ouOzWs+ffOwN47PdZH0t5mKc+z945EQnYzWUre6L4TTgJJbAaniS1SmYksiChOCaf3G5kcer6T\neqPrAMj92zbITf8HdBeo9pRw94udeW76meR/XB/JRf1MN+sRwNt/p4MGfzXriB4hMxY1TtYTxLmY\n9X36DMsKqt4wqBv0KmDau0IJCqZeoRyHQMlui8lfNa8GBc92dQNdA4URAUmdLMgjUEpTeHGAuDLA\nMIqydcfXyB9yh/nJv00/i23q3LS02qz6rm5KmaG5YdAq4PR4hnCQk5BaYhB7L/Jh790O3TxgcxCw\nPhB636agnNgQxGspg05zFErcAkxf1IAqXUMBhJwCTXXdScgeCvWfOx/8/EGPdhFkL4eeLYioSbkM\nN/sBzcLtuO37iwFuU/o1VWV6RugIB68D3V4rHg21PYxQ6OI9jTB4+72eJFFWeM7eT8lsLXEKifrQ\nixGYg1jiH32iQb9HacPbNJEjG3PTnJv1JOx1wxkdH8nbZAZKzeytbAOAZI9CBsHWgaPWw26cVqqU\n/ebeO+vxLazWuqr6bjkf0zsfi+HJY5qLcy9SrgYwOBpV6Fi+Q+tBRoDlG6xonL5AXQDkAYfS6rnY\nAO061+vXBzRLkcjt4wA8mouHYkpoVjC2UNsuYlIRJqcRk8c9NG9CtxfQp4i/Zs1p/YGwh26PsLgv\nFnmQRC5OH4nEbtYxr2Yk5FgGt2ybgbxCUPXiFPijm9uq7z2k5ezrGwHLu61lk/aUtNtrsLjX4Ma3\n1jbIVI1plj1u/z7j/R+YYH0Esfy3uS9qKTlYD1Crb25iUSfJXyanwI1X+/QuyKXoB/gAkOQphPY0\n4ubXGScvNjh+qcHeuylCNOW60OS5Gveh7bEx4fIxFsKHqWA8KgRWR/IOm804Cy3GafGQKNSNOEmC\np1ZpK7ZbsN4KgCfHKa9lIEuQjJgBwpLa9kkgpXU74YIp5K8GMFD5Tvxx++gDiThlD3Kr/HxAiKfI\nug5eU3/rec0K+YWoNyJd15xKViPqJO+jDSTK12hbNMlHbHOiU9ukdimhvHqeJvDsZ4Ruplu5S1um\nDxn776pLk3KbFRx8Pkx9pkBgjdv3Ky9VLdDBjRQ5uQGmxxGrGw3iHqG/DXDQ1McSs9F9+3UcxJsI\noanJF6iLmD+IWN9MOQCAAnSLVxfzuxgsUU/XhQQI+m7335DgL83yHNT+AWE8WTWQgKz9tyMevtLg\nNDQ4iBLOrH3tM2lpX/YzsjgSPS796SSqa6e+x9WtIOMhlounECAMw9Wlv/n+KRgDhpO/YBYOBDwb\n1r6cnCZVMPLQVt/IDtnN2tmQHLu9SLkyayVs4lct8rEGhfXXAYAd89cgqw6Wi9ChsHZ2nIhhK3sV\nkg8/AKEni4+3+PTUXh9ToBmaZXckvyZe3X5Ca5tlxPRRj9WR2A3aBePomz1uf7XD4RudZUr2E7+f\njaftUuNavVel6s4+d6J+j1NRJVa3JbZ++ki2YAMEFLgBXv/yr+MrX/67YI42Ce2eDYl6tHbvyr8T\nzv+xZVDqYG3WhL23gVtfi5i9xwKOx7FUI53K0i6iqVxyQ1lYtfeOBHKdvJQ3B/ZjwG/blhuR2114\nh4rxJP3X7UlOEJ5AYjIcqBTRl176a3+g7L9tHoS6f7SewW8RaH2+S2XG0YEAKsbR8cDFu0u5Oowh\n+c3VuFLnYvAdHScuI46nYEoX08QG5FgcsVGYO6oa/Ppbc0q2DTooL58GUt2eSsYsXQAXRMPyYkBk\n9G9zGDB/L2L+Xg+Nw5eLgFpEh3WEX5RV7PuokX+AGerU6ASURjTdNh4Apl00A9rkVOLrZw9TnMOa\ncfMTn8fvvv3f4w++8b/iB1/5XBkOnOIzmjXQHcAMdHUQWu0dqt/h5BFh+pBx62trEAPTRwHroybT\nabj9PR1rCskTQylACb304/RYDJTv/vAEN7/eY/K4z0uvXao82zuTxfug40MMvWXf6zWn9wNWd2QJ\ntm5EZAZxB4JaFzA+nsbW9hQ2NRerM2aD4AaYvSf7ZgCQzXYg79yDgE/5VrhJcbFyNRgDkKWNP1Rx\nJZPyI4lC6iAoPc+rFtzCJjq3sN2t/MIsfentMtfDBAuRlmN5sNmERUZtAwUnteKEsL4hCVtnj3pT\nXzSDkD1PcEFTCVC8cTCmCL+YErTk3ZFYdOzEsGLaCVukJcNUHAJmD3tMH0czWGqmYQSgaaf44R/7\nD/DOg/8P33rtH9l9/a5H/VTiIZqlk1yc+9muqT8nEKVe1CcB+YDVrUa8JVy6ehU49RlCxxJPoAxS\n1y70jNnDHvtvMNaHAb0yi8RofAYn/+4k5iMDJae1JZrSv5/KYjRdWOdzVZgdy4OC3k9VqJExPVYU\nOLQvx9gvAEyTfYF6YQHqtbL+8MDk86aOCL/zypUBhsIt6WkZo0Bc26qu7nCdlJ34x73HomAJKqAT\naCj7kGSk7qXElKgkTfx+TsU2ecYgdLB7CZCkidQv0rCfBkwf9bKIKmVzVlenPotk+y1BpbZKhzVj\nfdigPe2d9wIAUcEuirDfdK+yP7ORTvZ3zG2ahX38i5/+D/Htb/9feO39302ZuwM2NxqcvqCMRLw7\nCg6D+BLfbu13Tu8vAMu7hIffP8XiXoPJaUSzUKVcpHo2uCroyORuVtlbgORS1fwMew86HLzR2bNT\nWlhUZItKE6Y9jZYkJmzEeKk5GOR5CKf3A5YvRkxOqmd0zxlbB4yOPegYy/Wly7xqwfl/zbqK+tK4\nbpexWBNhe3a2QZiR/vk6bTxfDBmuBDBM3jyRD1R2oA/2GHWJaaF8rAaCgV4XkPM06D2Du9adB+Rj\nYq8gq1st3FkSleeDZQ+D1c2AZsMpn+OI+EgDVUAs7RvgdVm3vFvWlCSjnKoRLgmMPRcr8xD9u5/J\ndasjcaf281CE/0p+h7IfZ/t38CM/9h/h61/8Fbyx+QOsjxqsjijvY5Cko+nZemn1vooJ4UB5/y3G\n7KGEjDcrLqV78rToxM+VA7qzd6GCOTZgdpdizUHqY93LkpDzcdr6CzlPARKQXaY57UjGbSmBfT22\nE5fW68bYWCi0/q/HjAfXwtjt3kth81Ejq4I8ZdD3fQYAk1/9bVykXAlgAJAn3BZgy3oUrKOKGIRd\nbxOyfcKMh+mzVym8G9QPALu36nCc6/C0cnMoHoj5exqVp1JTEZ8tdBeQybm4E3B6v8H6MJhkl5Pz\nYNVdrbp9yRSs9SmD0ZBjdbWFDdtv8/d7TB9LOLTtgpwmd1iz0WgFlcP9F/H9n/lp/ME//lt4G69i\nfSRRkBomrRIprEuJOuY+Zje4ATEmTo57WDKepNIMDMpnjAcvLCxrlq6i9Dtcp92048TFhSC/i8wu\nVdoKoK7uMMIijMcu1EFdvlpKXrOkrhoIVeMkX+D6yh2rwRWJgdaqhqiECeCIMqtQILzELL8ywDDW\n+DF0BVD49+s6BvXoS0uDeODR8HU76guUrCW2wOJeyNIMsKxOxb0JWNxtEfq8DNr2qAyy4lLTngPA\n5rDB8UstHr/c4PH3AycvJYncysYpXpJzI0ljw5rRnkToSk6VkhzEZdrPQl4rwIk2M2Nz0Ej4tlJV\njYHQJeVp4IsuLTe9P/kkXv4T/y6++b/8ApaP3jIA9esIqJOkK3V/F+qFn8RNDurSEG97XZWF/cyB\nbX2fJrQPV091q+qgoB8nIT8rYGxBbQSaCWl9ELD5yBqTx+n3RgyPdVu9Z8ZS7Ncg0SbPV80MUILF\nAAjcOaxjKG0jUJ8Tumx3UDA2A/BF1rlofRe/5NkUv67f6Ge1cMzrZMMKtnz2rMJ9LoxIPHIdnERI\nZXOYB4ZXK7zbEhC3W3vSF/TZNutN/7uDBqcvTrC8LclOZw8Z83cIR99gW+ZtW9ixSJ7YyE5K5o6d\nhCRl82pOQDIQxVZVErL2TU7EWr+62aCf5+Omz6YJbMCbJPitT/wI7v+rfwrf+p++gPXpoxx74AZ1\nWFfgoH1IGEh+boD1jbyVX5wSlnearNJo/+r7cTERRf0MU6/gnwGAbQLkkrPECdlu5HqOGQ05PzdF\nRjdXsMnjgDYwVUknW7EEm1Cqga7I/bMh09tgTAXwYxTld7FXqXGZMJb1WY2Oej/pa3o2jIGI5kT0\nG0T0u0T0RSL6y+n4HaL/v703jbUsu87DvrXPucObauyq7uqBZisk5VCyJaub1GDBlklJpIaIihMo\nDKxENugQAQREgX9IFAwYiAMBihEpyp8AoSwZhBSGohPLYiSFswzBaXHotimZpIamOPVc3V3TG++9\n5+yVH2vYa597q6uaXdX1qvttoOred+855+6zz95rr/Wtb61FHyOiR/X1ZDjn54noS0T050T0tuvp\nSOWeNOGwwj5bIsuESVKZFsNJFI+nFZMt9iVK7tCn3MoO76nWQt1EU0Ut41O5GOoU7ATPeTjay9h6\nvMPacx2mF3sc+2qP9fNddU9mexvLLS1Y7Xx2N1uF4qtp0syzF+9B1rTyGZpUFZhvJhycajx3ZJVG\nj81uhV6LcfLbvgsn/tp34rEPvhcL3vcFa+XqAPU0RMKPjXV4HpZ5up/A0X8LT5fgLrsZLtexUoBD\nzU4FphX3LccvmyXzrQaL9SAUBs/XE6mqrb7YJGDWyGIeA2mWllF+Oz+4Vt0TNmy6+KOmVTCB0CfD\nbpYEnaQarC4ZSH8VlmMCW/GTF+uR0OG4ZpsBeAszfxuAbwfwdiL6LgDvAfAJZn49gE/o3yCiNwJ4\nJ4BvAfB2AP8bETUrrxxbHDSs1grcyxAX9PBB67VW2X6xLalYwwdhHxvZSI/fvjfVHgk9l5OUYzck\n3chNBkjFnandyxjtSLCVLXKw0JNzq1F9FtjDwOx4g8VGcrV4/44GsxMtrDakZz7uS2ovuy4n2fmL\nFiap5acXekwv9JI1aUKFEBV3LN0NzbV46nu/H+v33o/HfvvXkfuF7ILjegcdbxOa/TpPhY1dWgBp\nQVWFKfOElN8sfZYdOBT0NUpzhcwXTc0WRLeRPNu2PD9Ge5AxuSxU86F7uRTSIX+O+3dKrEyeMLp1\nxuRi6GPUInosAdUrNyUOx4TNZJUWXIV9h2cxO0Ho1mUesI6NCWgE8HHV2nix7ZqCgaXt6J8j/ccA\n3gHgffr5+wD8uL5/B4APMPOMmb8C4EsA3nyt3zn3Sw8tkUCGttgqG211p8Pr4CGVOgZ4wQGMQGL0\njvQTYP9UPWxWUUoCjjhUCSrXl2xBg99yoSHmgIN/NgYNsNiQ6sy5sSQjjJ17CfunU5hstdpM6vM3\nd2RasAtTq2lhx7b7uU5HTijekQaycFU4Jibc9db/FM3aJp780PsBJUtRzCqdJVdju6cCIIwBZSpV\nuXQH9PwMg+fFVALX7PNi8okgMw6D37vOGU/97poU1QzR8DsWGJcW7AlU+7E833ZPF1vDrvX5vAjg\ntN0/D+/jGgtz6OIdenRiM411vpmqPB2uYfQBS+FyXSag/cSL80gA14kxEFFDRJ8DcB7Ax5j50wDu\nZOan9JCnAdyp7+8B8Fg4/XH9bHjNdxPRw0T08AJWSSV8P7C/qnODi2q5s1e5CS7ChVtgsclYbLEg\n7IPj4vHxc/NLz0+EHZbg3ANT252PH02REL8Q/5ZdzbSLwsXPY8lMtHemQbvHWH+u90hM3wkJTvhZ\n2p18Jyo3UeU6DFGi4+1eBIG7ZsuOb8CZ3B+QcsK9b/97yHu7eOZjv41Mcq9OP6eyC7d7hHaXhxj/\nRgAAIABJREFU0O4RUkehT3K9foJS5EbBWZCwEomFg7BYTxUuUHl2QtyCVZzihrwaGGV274svJiWQ\neXxJaDY23ZTQzElYjnof5bfDnIyEpupCZfyHbYnHcLXjcn2sPeNuTetuKqM29mvJvYmiXb3Ydl2C\ngZl7Zv52APcCeDMRfevg+xe4xate873M/CAzPzjCRD4cTnDUAiKyw+TDVRe++t+LDcbeG2bYv1vs\n+DxmzL7pAHv3dUu/61GeGRUtF5CFcvm1rfYrEJXsHqx5/EeY2ImKepwEIOzWJIP0YiNhvpWwd7bF\n9t0tckMYbzPWLpYYDOqBE3+ZcfyrXdltE5b67+Alaw4Dz1MJ92BYf43w42OuBWG6acL+HclTufvC\nn7Q491/8A+w+9RWcf+RjmB8XITs/xlJ0dSLHtnvAHZ/vcOLRjBN/kbH5dZYsRaop5bFEn1pGbGNr\nRi1B6lWiLEBVoYXLAVhyV3/WJKBsv1bKxJudbQCeqPNFUJjplUdJTSsgzQQ/SQvC+AoVgacMUVvU\nltTHIlivuthXaKe249vCrjaiFRqzYDOF3eqfD7Xf6jrXUq9XtxeFVzLzJQB/AMEOniGic/LbdA6i\nTQDAEwDuC6fdq59d+/pBIldoeWyr7DcUoeEg5tCEYCBPgNHaAjQXl9XoSsLkL6doT8wltfmK6w6v\nY6h5noiabwCa28pc+mJquZ9PZbFaqm+J4FR1tmcJ8Z0Ax77eYbLdo58Kx8AmEWXGaLv3PA+AqL6R\n2ShCj9zE2TvTOONQOq+ahBG7gpkheRgLJdgSoLoZp+g6Tk1x7l3/Da78u8/g8mc/5YujXxObnJNk\njMojwni7F8rycz02H88Y7ervJq3qxIDVa3CvyECzqsYwVvZSl6uZPPZvsUbYO9Ni/0wr3oiWMD/e\nOE5jnhw3CWwMklDNbe41B8vzwcajeFAKx+WqZq5rjoPLDRZ29E4MvRJRax1et8LIXg6NgYjOENEJ\nfb8G4AcA/BmADwH4KT3spwD8jr7/EIB3EtGEiO4H8HoAn7mezsRIsermhve2IuDF7b2h14LL96Mr\nhO75NTRnDtDdPVPmGIDH19Cdna9UAStWIcQMyWOgHwmvoQLOVviYfWKHB2dsNWPj2W65d6ZBs5C0\n6WmRsVgvZsMwVTonzSrE5Rryg/riXA8BF2dbjSPu/tTteiaQ7R4bifWfH6cqQtV2yG6Npbzc8WM4\n96534+LHPozdL3zexyu3IiC4kSAk05ioY4yvZKyfz9h6PAMkeS6i67jKjhSeXXz2UShY38VcKPfU\nzmTcD04Tdu5usHNPKxjQwkwf1SYiM1YXfUxzXwXw5bLDrzJxr+UWtADBylvD5ToRY/DfCUCp411B\nWFQkq1Va44tM0GLteqIrzwF4n3oWEoAPMvPvEtEfAfggEb0LwNcA/AQAMPMXiOiDAL4IiVb/aWZ+\noYTb3u76lYfw9M98T7XzeTUkbabKpb6Wnq7qWlthUlAPrD/WgJ9Yx/wEY3bfHFgQxs+2GD0zxv49\nPaZPNXXMPeDhv0IvZjT7hMWWEF72zjbYeKYLmYAKwSTusuCSZUl29KTqq1RfWmwQNp7u0R6Iy65b\nb8Ak5dl88YaJkMeS02F6oS+mSrhn81TsnZFEJqOdXCaJjiHCxANDIvf0Hg5ONphvoQgNgteG8GzJ\nBIzuOIO7/v678NSv/yqa9XVM7/8mVeflogenCOvnSTI4q1BsZmIaGbbRj0oCGrLkOfa7ycwYPcYB\n6DrFnswRGftuKnEp4ys91s8Lb2O8I8lyTXBU3q0gYGbHk8Q/dKKFeyJgXEUYrNAQrgogenaoZSHi\nmgJqYV3GQd43c/PaJKRFLoBxOD/Og28EeASuQzAw858A+BsrPn8ewFuvcs4vAPiFb6RDsdIPENRK\nGqz1q5kYq9rA/qIMTJ8j9JfHmJ/M6KeMPGbweoeDM4Tps6kIh4E0zmPJeNweSHGU+RZhvJ0w2svV\nDhcle5pL3sSYJdpLrEHCmNsDLna+Rvk1cy2XRgj+fVlc880GqSsLJpaxs4k0P9aAMiSRbZg4kcod\ntQf5jhScperzYqNzPdYETF5zH87+l38Pz/zG+3Du3f8txufOuXaROtFsRruFrm2U7nZ3kGIuPFvL\nwGSmlyVnLT/LrvrnkWR1Mu9PtyaAp3k1JIeErsZgkvn9Jbhw6IzrYHNOx21pMb+QdknL30e6faUh\nYLkthW8HjSbN4WHVVwUy43z/BtuhYT4O29U8D25/BZrrNQkcKwbQHs7k+YTJ8wlrTzcYPT0GH1vg\n4K6+Zj2GxSH8d8EGZqcy8lip0vF3KEwOlfRWQgxZVOpmX3bwfiJpwEc7uVCsWYSJ5EtAoWGbakhi\nB7cHJceh/b4Ba2BgvkGYXM6+gIaEGqNFA/CIRcrsRXJWNY8ziWPOwPo3fzNO/yfvwFO/9qvoLlwM\nO3tJAmNjk1vREoZmj4+hPXNbsAi7KOxetL89l5gR+61E7pY1sDWPkpsSpoH44tVrC74QxtJU+zgX\nhvPxanTjFaaumwM2h69yTQAVgSq6QSWPaK5NhDjvElZqKy+2HTrBEL0Q9hpZXdHeS4v62PpCg/em\nRSvYxGlAZ2XBINYfnSDNCP0al99M5bx2n9BPZBKOLybs3N9hsUlest41iwn5ojYzJgqwPErIKhQ8\ngKlndXfmqs/OYgOchMNJEq1YyLd5HGy37dYT1p8r9SPM/Kj7ELQQU9NJcAE7zipfA2HHDAIvTsaN\nB74Dx//238ZTv/ZedPs7qvJKTcydu9rK2zC50suYMWARk7EalAGKsX+2GfQT0pyWYYNQ8PXgZJIx\n7RhpZoQxdrA1j5Q+3gVNRf8dnGwGQGQQpMGMs7Y4zujX6mm3pFXoq8/dgQYm92onl+NrnEqu28wM\nqC7Hrpz7K+jSL7YdOsEAFFXczIihdhAZkJEWKgeECw1UqqF0XzmoLFpEt1lca/G70RVCnpbKyc1e\nwv5dGQfHU/FAaN9lUpk6XBJ2xpBpU39t8rhZYA/fqNcB5OynSSaIukiHcQT9NKFbk0SqPhTZSE8q\nbI34ZO4+FSiLjUYwnEWhOKdusKujnuSgotYe/1t/G+vf8kY882u/jjwTfoq58mRc5EDLf+lagz2j\ngMM4cBu0DxHotirK/TEJ/yEtJG+BpL0XDkQZSz04eFms5VaqhEfbPrISc8AaQMD8BKOfMhZbuWhD\nWJ5jq1T8auxQNBM3XXJ9noG/ox32qlwRbJcPgqAxofcS2qETDHf9Lw+VhW/utIgExwFZsRM70j5U\nd7H80K7aWBaEJeGortED1BHyWCT36EpCf7LDYos8/TuAoDoTPImKCYQQDmvuThMkHv3nx1GlNXEi\ndFMxETy6UjUGi1rkRnaWwhQNxiyV483k8IKuDbB3Ni3tVkt4y2AsveSctpM//KMY3XEG53/zN8CL\nXorULsRjY9mv90+lpcVpvILqt4bCm6P7NnyZgH5EmGzbwhGB4OXtSLUty72AIhABYRT20zI+7jJf\nARTOTmf0E8b4UhJsypPJLPd1aSy5HGu/U2kPQ4Hgi12eqWmTFfkpoZiC4fP2k98Y8AgcQsFgLVKg\nIxcfg0GsJm44dyXuED+7hrY12k7ijx/saAAwPZ+w2JQPmjkweXyE7ddqCfpk7LuwmwX1NAb3eKIX\nM3OUVm07R29pzMweToKaT5/v4KnhXWDKRdsDicGQxDBwUNLv3xh/BNdCqJd+XfjmFrPjWvlpIh6I\nbk3+GZlopXC1Ca4TPzHhzH/+E0BmPPvb/xJrT2WMr8iJ/SRh744G8+OEZsHV+RFZjziCuxZtHKM5\n1Gq2bRK+RLublddRnrPNI0uFV9R59ueyd6cIVBjwvUoDhfBX8oQxuShmEI4vCq/iOnfpq5kU3oZz\njkQojHfystlhx0UwdZVAfZHtUAoGJ/N46nR59d1xaPMOHkhuliewC5EVAztsZBqD0aVt99B/SdPK\nW/KVNCPw6TlmJ6lkMo5SH4qqx4Iog+I1TMVLYWCYsymT7HbzrQbTi/1SnyJoWXlxLH4ghogDLhBM\nq+mnhO17xEFFWQTCYkPZjCcY3aYwGqkD2t1wLddi6r9BALUN7vrJ/xrz88/g/P/3e7CIzX6SMNqT\nDNURqbddsJ9QGR/FXCr73nZ+y+PgAon9t9NCvEBMKJwXRmVa+bhAclxUeS/C/QxdlLPTPSbnGyAL\nrR6XRkVrHZgfVRsKgKhJ2FyJvzcwNUa7YX4E7cSDuXIZx1ck+AgA5375oVoianUoH9zB4FX4QxAI\nyTCKweer2ipzxBaJT8jwm6MrCkLauZfG2Lu3x+xYKGEfVEF3y9m1kuYGGKiFFS5i14h9V0EQg4ai\nZ8IZcl5FSSa+vCdYPkW7fh4TrtzXYrGlWsIE4sJdZ41jYMzvnmP2Hx34ImxmVKHfQ06//Rv1U7zu\nB96F55/6PJ748r8VM2DBGF/pVdNj76OBqoZ7xOeVm8KKlN/T8ywl+orxq6jLKMKDemi0aXHz7p9O\nRRPR16VFTAI22orp1xn9yQWm55tq3CuXZbxEfD5hUVfab9B0hwGDo91iEsU54iYMlesRf+P8BWuH\nUjAAAwmsEyUShoaSuZnbifqqu2fSEN+ShGP179VqrLxvDgRLiFLYdtm0APqpCA4mKTUPAJdfJ+qy\nF1ONGkrYpXzxUrlmhUrHMOgEd2tK/wJZCigEHZsgIa186mQhyr0rh8J/l3Dhr7aYnQT278zo1hnd\nGiMdEGhRPBjp4gi4OEa3YYlUpVhuxB7MP08sAnm0Q9h8jHHi4hq+5bv/IR7/8h/i/JOfg4Gj7b6M\na+RdGLszW5i4jQOhaA8ZsqhNGAbTQ56j3ntf8iBGjcPP08ewe6eYNTY3jCIdTVkwMD/OyC1j+nSD\n2dke/YSx9pXxdeFWpoGuwiCkz0GQDAWEArejnR5DsBWD826EN8LaoRUMTgsNO5yDN1fhL1iYbxVk\noum2qgGM5wx2u/h+mOjUPjeEerQtFavMpGj2Evo1xv7pVAFHEVm3rEyU2TNGx3RxAEq4sbZuTchA\ncUL7df0kVPTe5B6LsKsyPGcDIKxIJqEvp66g93bZ3Er1pbSQGhuLrSxl1jTicLRDSLMyhmkBNPuE\ndocwvgRsPNMBDKxNTuBb3/wP8OUvfggXLjwKboTBZ5ogsUaNap8tc3ZlAkUNMoKm4f4tVHypiG14\nDi5UACw2GsxO6ZeBeDR0LXISXCHNCYsNEZzTZ1PlNnyhVgnQwfxdhYcNTYrmoESHutBEuYb10atv\nXUefrtUOrWA498sPAUBlM6/yJUdU17gNaYHyoO3zyJq7ijCQL+vjcouVA82N2X1AP2X/nXafsH+H\nBi4ZYMaDST64DoCSEp1R3E0kqn6rmgINvAjlGkUDANc+eqNo50m4fgb2zrY4OE2Ync5oZuSRhM1M\nEq20BxouPdcowzmBejneaNHUS8n6yQX9d4mw/hTjxKMZJx9dlD4TsH78bvzVB/8rPPqZ92Pv2cdE\nMAStx1LWRcCtzuocbxgOTrpXR3NQuEA0DaIh/9u8MyYsL9+fims8qvrx+SRgdkfG+ELCaJswvkyY\nPJ/qZxnV+xUtahVDzKLiMlTCDypAgY1nsnuSSDWDCDzb8TY2L8UbYe3QCgYAZXJHsFEHzAHKaFIE\n1XQIVFpb5cWoQKOBPX/VB66/P9ourD4ygdRKth0LIwZqb0RFQ7ZdXk0GuBpLhYA0BFkHkzImXnGV\nPod7ZZZ4BIbvOvMt6XezL+elBYXji7aU5pJLgXoxrZoDDXpS044ynOswusLYeqJ3CrbX4NQFefyO\nb8Lr/trfxRc/9S9wsP2cC7NoIrqHJWg/ANwrYrhNBHIrF6dpRgOTwcbK2uxYIKSZ2RDtev2XJ4x2\np5QqXNleYJeOO/gSeB602Oi5igD5aFvKH9rfXreSofkuU61h3CBr4nALBnvetnA4DDStGGDIonTK\naS6TLV4z7hByUjlmuGOkWfBO2CU4PAQ1KVjNBctoBJIMRbZjNvMyqyr1HigcB2Mu6mIXGjVq+u6g\n+c5p1a0r88JmpOSXNLr0/mkJw+7HJYGrswjjrpXJZ0jqyc0eSScn1Z+tT+0uY+O8VkdqJJeDk7Ws\n9kED3HHuW/Ga1/8APv/pX8Nif1sTjqAynVxIovyWu3ZRv7omNhAkUTjkoI0Z4Lp3Z9J7HKjj0S2u\nAr/doxpzuI5W+CXl76rvsauRwKUbDivJbLId4knsvhS4tXFFlXzm+vv4Qu1QC4Zzv/SQS1MnkWDF\nAFD5zINUosqmiyo+qJVthbRt9wizM0Pan75QUfdMi4kTYP9MEsJRZC1OUlGhbfcLKnUeU2EjQk0B\nS+/u6L2qxB44xQXDcOFZtIjotuOGMDslMQGWgCbNUBaXTjD39+uY0UK0h2ZGyCOumKlrzzKOfb1D\ns59hQV3tXg73AKRZRpoxurWEs9/83Th773fgPzz86+j6A4DhNRmR4Nmtc1tyVzoJK5YHVAFqfy9l\n7lbsxvJEUmb0U8Ll1zZuCnl9iKHgJQkYM43qelyAQxM1uhArrYLKXFnl3pSwcGB8pQDH9Q+Va8VK\n3ZxujBkBHHLBAMDV/KqWRNQQzGOhny0lzBg8DAclrxEIXu2cPWFxLH6AJTfRaJucBQdAefmaH/Iq\n6qxPFpvgVNyaFa3VtZlgjjAKD0IXgWV8joSnSLcFlBlJyurM5fru/bGd0iZfeLUMz2BU2ZAn270L\nrijkrBakZUfKY1mU3RrhNa97K44dfw3+9JHfwKJVMlZj9TalKnenFbNMa/LQ67gwUgFqq6QuQI1L\n6Nvds41rgBWPAkXDlOtiidG58hle5TOfs+EZRw2YTBAD1aYm/ZbNZu1iX4hLQZOIJlKam6eqnrMv\ntR16wRAHwvIvLKn9YeCrQR4IBzdD9KElJaY4MDmQ+NbGFxMWd3ReuzL+vr1vZsKW5EZND/39xRZh\ndryAVc1BLpMk/qtuGqiEweC7QpIK982yKKXGpkUXoghEfX9wUvpYFfhNxVyysHdPfmqvudwv5VJM\n16IXo30vgy3UZQAOmu3d0aA9kLobKQOve+OPoWmmePSRD2AxDSzGBphvKg4SMIXo0pQdnXS8i6lV\nJZfVz7iVcewnCYstVCZB5YEIGp8R7K6HQWvzKmqolYcszpX4Gw2qzcqeQ1oA609ntDt9TVpKatK0\nlgyXYNmhb6QZAdwGguHcLz3kk9ckekSurTnYNhQCNvnjsTbhQ+TgsMVrpTmw9tUR9s91yBqOXFFu\n9f1oB2h3CItNlroJvfR3927J52iZj6PAMozAwcYgFFwryOV91BIi9mLIfbQ/84iwe2fr15sfa3Bw\nWrIyuZZhKm9XruVjo4FPMWTawTP9/eglckEVZ5VqIgenGkwvZxz/8kJrQRLQNPiPv+2dWOxfwaN/\n8f9g+1zC+Wf+BBe6J3FwhnBwmrB/h9T/tMzNANCpNkE9NPtzeBYWEKa4C5IIqDwibN/X+OZR5XZQ\nLck3HtOYggZx1cZlDKt7pvrcpe9RBI+ZynkkG8zmkxlrzwv6K5XLyuQVs1AErSQfZh/3G2VGALeB\nYLBmAxsjHk0YVCyxIKErr8RwIturqnRDYVOF3OpvrT/eYna6x2KLa3Ub5QE3B/LAu60ABDKwc0+D\nbj0VYWBYAcqCL54E9mtGIM6u5dGX1teG3N3pYdZ6r2sXehi2YHkj3L072Pls0bjKGttAI/DzByYV\noDkoQ67L2fEGaSFl9dxsMvOkGeGNb/r72H3iL/H05z+Bve4izn/108itmDuz48DsRJJU860mj21J\nIiw5aFSmkc1ysbsTXBgfnEzo1pcX8RDAzrpAueEX3IWXuAe6YaUeaA9QtIUwr6q/B8LdNKPNJzOm\nFzoV7AlwDEkBZnu8XdAaw/3fqHZbCIZzv/RQ9XdUkYceh7i7AajUrCHdlFg5DwlLZKbq2LDAp+cl\nZt+Sx1auVP19Y0HmUK+wnwA794jmIOeVIrLSTyph1A1VAi9W1RYcoBSqtd/upnWqMzC0VgLL4jyR\nsNioBZ6VSzOg0TQc1whU+PnYBA0lxZoRCP0j9cCQ4BlSIEVzR0S3qworboBRM8X9P/ZuXPrcpzBv\nO+x85c+L5pGAxab0XzQq9mtURKZoDmRGnhSeQ7eWcHCaVgoFE4L2vPt1SWZLXW3CLZkUjGXBClRE\nqaF5MjRBohYMAqbPMSaXOvcueag8wb03Fb4VfvdGagvAbSIYACx7GoYtSHfnOwwAmeifrkAmrSbU\nzAbX8BPhD4B6CMnlIqFbZxycyaJBhH41c2B8iTxCEVAi1BjYvi/VAJKr71z9rk/iJCYAt+R5HvpJ\nqoRAmivANy7gnRTQLabL3p0SVtxtskQIjhmc2PswRMXjIvLFY4VVmnpCD9mVlnehmcuu5si6CQTT\nlLQ4zxNfewhf/v1fxdZf/w5cfOTfoj/Yx/z5Z/343AIHp4FLr0u4cn+DgxNUgFZLTjgkCFl9jpZw\n5TWpShVohDdD/+1+F1uM+ake48vkSYCquYOymUTA28ewK2NWx3qU4+qYDzh/ot0BNp/qfHOQxU9L\n1yoClap+3eh2+wgGXUyuBhtwaLn8jWwz+BcfqGMLcdewHVAnglcrHu4I3pHydrRDEpM/AvbvyujW\nAAdJWbAJJlVN2zIp9s40jifkEVVPwTQKC3aabzUY7fZVOLYj/6Ev7cyqJRUtxKLxDk406p5kd11W\nEYRq4zpwymHsBqZCHqGYaMYRGSwQaxIuDRcWLhDM/Uoyyc9+8/fgzrf+GBYXngcv5sgHe7j82T8q\nXpBg3nVTyUc530xezg9AHVDlGoncuwuFXBZj5LewukgXZxcYXWquTmQKzz8SkgB4pqul3fwqQre6\nBoDRbslg7ZhRNH+DyeG09oGguZHterJEH4p29//8EJ782e+RvNOIu1W9ey1pFYPJKieHawSbGdCH\nq7u7063T6muYgJpcJMxOMw5eM0f77Ajjyxop2AHoZVfKlsWIhBXJqcHahVwXqiF4cJG5H5sZl13W\nJuHcmG9wLkOznwtuAjh6308FW5DgJ+EvCGhXJh5I4iIMEe82oTEm5JmXAIm8dPRdf59UuDW9pGKj\nxLXwVFxBxrZ8YfcjXIUGG/e9AWuvewP6vV0894cfRrN1TEBK5Ru4pt4C/Rqw2IBWi+Jy7cFznx1v\nsH+GyjzRn0/dQKABmJ/IoFHG6MpglUUTRYPyhkFvbpLG6ZGKVuKguS5i94RArjXaZqxd6CtXrwhf\nro9NkEApNzV0zrzESMpV7bYRDNbyCCsxA3sQBtb5g9DdKkp3V4UHdpoLGBazwkuaczjuKlrE5LmE\nfHmMg3sWSIsWo90yIW0iWkao1Muut9hspNLUc1lAwwyQzwxBnav4CLOD15ImQNX7tLTkfRGQlBl7\nd0g8hAkpynJf/VgWF1hsaiYxj5r9oJHpa7/GToPOTREeRvGmThiezYEKKqVBpwUXIDSMU4WTaEWp\nbprcU9Ksb+CuH/zPwAnoV+y2ZvYd3CG/0x5gOdiNhfK8d1dIgMvLAsEiKfspkM/MMf7KdOWztZ05\nLco5gC7ewO1w0zVqtrmcP9Q2jN24+WSPdneZRGe0Z1giYAYwoHpXXI0b2G4bUwIA7v5nDyF1BTSL\n4M6Q4mwmRqxqZCqzHxYe0JBkYg+94sivEgqhpQUwfWKE+dnOk8XECWLvza4lFu1h557GS9LLhZYF\nVyyQYpmOVwoslmP3T7WYnSo8AFevTYC0shv3Y8b4MlWa0zBuJGaMFvuclBsg155vKm9CXZxpXps6\n3tcVng5PPmJq94o0csNxLyo2uVAUTgO54Nk/kyrvStxMrGgQJ8FcZnct0D45Wa46ZfesGhbs2ekY\nmlCILvJq/GzuhWdpWpb1bbTDElK9qik5rHREPx4ncJvQrTcYffTh1ee+xHbbaQybj2fsnU3oNoCs\nuzEA2f0U6CMGOK8mLvnkMFXPdvSAYQxjITwbtdqiS4CPmTOkx/ZSkKZfAKPdUGuQ60lhu3w3Bfqz\n4pIb7TJG+2oahAUfU8UZ2QeEUska8vuzEw0OTiTPExG9G6axcCuX7Y73WHuyrcYnqq4ECbKaH+fC\nBCRGmhWtCwT0a5LOvplZpKNeI7qRAVihG6YCGuYROVvVA7OCkPOOhL99cyDRPBgBayDCfEvS35t9\nnkKMByfRlnLL6DYYeZKRdhuMLoebDgu9wlsijhU3HLtXfc4+nKa5ApWwNSp9uwtML+QlAe/mR5v8\nuzwuofzdWkI3EfB1hY5zQ9ptJxi2futTWHvLA1hsNdi5u8FiQ+3mXFN2zQbmUXCtRVUuqqhhoce0\ncJQLouyAm9W/TeG7XF9j/bEW+/f06KY9+kstJs8nFw4xyMpBPxVO8+OE+TFStbVBM2ekuTIFVQBm\njSNwqnFLyCO1vSdWNbsWbNELk4Ngay83jjXYYo7vrY12CPt3d2h2G0HsexmHWLtx70zCsce6sgBj\nMhWyPsmO3ltqfS4BTl7BGwDiBjoQWoAclxZAu8c6/qz9IOzf0WDvzqApBQ2JsmR3XpxdALOE9cdC\nQc9w7/68A1DNDbw6lX/mHSvjbOdGV3p8FpYRbP0pxvRyj+agJO0tXgo13dRc65sG82PJK6yP9jIm\nlzOmv3tdlR+/oXbbCQZAVbDtHse+xlhsNphviGvQhIPl3s8tsHdXclzCQSCT9rbYfeKWvw29j3Rg\noOwWeRTc8qrBxkU1fr7B7K7s/nBfaBq4VNmjKLs56QbSTyHU5g1gdrIpmagMCByheAmCZmQ4i0+y\noMLaq41Vu1/79qMiFMlL1APtTlMzBrkex36NMDvWYHK5BzKQp6SgHFc7ogsCj29AFXrt5k5E26Op\nBGgeCPHEWPSmpGhrsH+Wwm8JBuGmZ5KUfKPt8dIu7TY8FU2zKq6rQkLS3elmk8q5fi/RFY1yXSYR\nCmkhQWfr5zs9llUQEBCqlHESeng/luI87YwxuSzFjFNXku3crHZbCobRxx/B4gcfRJo1xoN/AAAg\nAElEQVRnTC5kzegsOlvqhNSTOq0GTWJv2gONKbaWUqpBvjNX3NXcQO56DCizCxPVAtJc8AZ3fyLs\nIEEomNpo5kwBD1UjSfI7/RT1BKSibRCW++qVn7V/xIXVB6DgARFlH4wF6ZpOvSwoC2n3PobFxZCK\nXOMdkTIxgxShYA7EXOjX4TcdOA3PwU2HufZ/VLSryeVcPDYNYbGRsH+2pHLLY2GhRne19EuvrZrN\n0Itlv+WLO96jYVGD/pnAsPsaeiIc0D4A1p/JmF7skea5aAf6+7kh9Mrs7Eeazm+PMdnOgkMEr87N\n8ETEdlsKBkAfpoJ1aZEx2mHR8zTc2LInjbelGtHBKdEqEgDO5eEPUfgI4vlvGf6giLi77FCuMVTZ\nTUOp+muvYZdy8lDQVMh+045r6v6SwQ82CY2sM6qFgV8fkFT4+rS7Yxm0IIx2ZUyaWd13uQFdhKqB\ntPuK4UTBYAuBgKQL7cp9LTaf6osbFtDnIf00JqYnrmGt23lAkqbe7ls1pLXnGKMdRj8m7J8hpCwa\nYW4J801Cfwew2CDXCjmJW9WFgplQNsbBNHQXY3w29uzD/S+p+uH5R7yqMl2CUDBPzrGv9Rhtd8Ws\nzFILo58mdNMSKzPay1i7kouJRCi5NlSzuNnttvJKxDb+yMPo10odB48vCMU+qxL1AYSjLJM67n7x\nQbvqaYsYMumHu3alKSDsoLFdTesIO1BVKi98t8TUVCFi5fLifbmpQINrNGJXZ8VhqAfSAYHbkhq+\nH2OJOWiTf3aKXduK9xLHM95rHknRmtwWijdVocNUIe2mPY0vM1pNlQcWYZW6weLV+1lsaIXwLcFk\nLFK0H4sAdGxgKLzD+NhvR7wpercq03OwEURtyb9Xzc6HwjQO/Te5yEJUC9jK/ESL2ckGBydlvMbb\nGevPdhhf7kTqZ1ZAWwWEzvPRx2+utgDcxhoDIGnRF6fliU22JTlIYtEaQJo5qAemF3vkVvIA5EZ3\nt4SqLmOMIoyl8Wxx2qKKO3ylKejuRx2QonDhejIOgT1uIGAbYWnSuclgC9B2wGC223GuIZgg0etI\njgjhH+CA0MxKirY0I8xPZkm80kk+Q+tzHsl1jMPQjYHZmR6jS5L70PpjyV6ykZ3G4mno1husndei\nuqhVcCmVR1qiTjwKkysZaZHQrUGrV4uAn1zJ2DuT0E+oUIsBLLbKNV0IjoTEBcDzRcTdOeIpUchF\nT0il5TXBY4VlbS9iDLEvRAWLoAxsfS1jcqmXILAtnYctoZkzRrsZa8+FH9VgujIXWenwvDR3bma7\nrQXDsfd/CrMfeRP6ccL+qYTZsQbNrMH0Uo86GzNjeokwOynU4Firwc2EsDNUiwzwBWvq8xAYc0Cw\nKa4xNw1ewANi1/bdH2EnNkET7G8XEBHg4sHE1r4bjyMtSJLItKJiW3Ut6gFqJNdEHksdxtnporqm\nhai1VVwEy/kgCS+P5B3bNSmLUMkjYOfehP07EjaezhJE5eZWyZMZ3bDNXA5I6oVZrBF2zyZ06+SY\nibundcCy5pbgppgPNh5LZqKdFp8f6v5XpDc7v5fFXpmahEprMlc29cVt3syAtWczRvsZ3XqSPJMQ\n02n9+a5iNloEpXkkiAFPB6/p25BfHm0BuM0FAwBMfu+zmL/9Tdh8MiNPCIu1hJ27xQ032mWvfDy5\n2GFjQti7S3Ylc4/5DhHU+aglVFhClodvi9GZlihuTps8aYGlxC6rJupKDSJoIku5DFH/Tuyzu1S5\nLFA5QV7SDBjPCN0Go19jdI1knrIM0JwYlnnKtAXpk/xQMxOA1whafm3S7upYGhkIJObX9msS2r2E\nyaWM8W4ueRQUCDT3ZjvLSL2U98stoVNzwcbHsZBgLhlNO83hfIhISCN9Nc2mIqsNTYwgpJPSsE0T\nck6EUfLtelG7DEJq7VnG9IJkd94/KTk2154XhmsUhoBqBTFFm5Y4ZDXH8kgYojcbcIztthcMAABm\nEAjtnmS9mV6Q/ATdNGH3TgmiGe0xxtsZkz/PmB1P2L07Cf/BFlFb7NnIKhzy2iPGIG9Uq7RJHkwB\nqN8bqHGLuCu5CcKr3/strhAkkQjku6BpL+Nw3EC4tLuEdld24W5dLpI6IHVUhFpiKbaj/YxuRG4Z\niy1gckkI3F7811ylKP0g/a9blwQre73EbaS5pozvZQc1AhU3QHdctISs2EcamEj2mscALVBpLktT\ng0xzCgIralkBK/BrDLUHe1wDM2RIeBvtApNLohl1U6lwNb3E2Hi6aAcxw7NrAhDzKo8SqMsVdkWm\nTbzM7boFAxE1AB4G8AQz/ygRnQLwWwBeC+CrAH6CmS/qsT8P4F2QqfLfMfNHbnC/qzb+yMPo3vqA\nJLZIBDAjzTLGC8Ed8ljcWXtnGjRzQX23vp4xO07o1gjdulwnj4opYAseCDv90KUGlIUcciDasXEx\nmq06NBXs+EpriBMvmAmpB/pRmaC+8FH6RVT64jtdEGixpYVwGbp1Rj9Vtd6EiJkkllZe3bPEALap\nmCwBB2HSimBR4wGWQDlOIij6qVx7fozcvHO3ZOQ1DPAC1xSCyeYDgXKv7nkIZqMDjFyehY9jyARF\nHUCpJrjZd84fUdypPYDW4mR0U7mX6YWMdk9dt+pRcI+T1oQgfYaURMCmeRbtQNmsHm2Jm++eHLYX\nozH8DIA/BXBM/34PgE8w8y8S0Xv0758jojcCeCeAbwFwN4CPE9EbmPka6VdfWhP1y2ZAWCiZ0cyA\ndneB8VqDxXrCbEu+nFxhTC/Jw1ysA/MTalerTzpOuohQA3CwcmmnDwQqZzhS+XvVznQ1z4XfW1jk\nMYOVf284SQ6aSTwPoZ9DO7nTTNgnM/o1Bi1Iohb3CTSrtSf7rRTuw7WVKORMHQ8JXqrdnAApW1/Y\nqkyoZmPlItR+Z3M9ak5NGph/TtSyPkWXcRizaPbFsSWGpp0bHG8moT27vmTqml7MiisIoDi5kjHa\nzZV3DJll8VP4GwCSmAgWI5PHojGwJvSNmZte7nZdgoGI7gXwIwB+AcA/0o/fAeD79P37APwbAD+n\nn3+AmWcAvkJEXwLwZgB/dMN6vaKNPvowurc8UNxHjQ56S6BOZke726PZ7zG5rJmSxxLX303FTz+9\nlDHfJMxOKJMymA1L9mk0GSh8ZkAWsEQcElUSQKD/rgTFuH51jWKVAInHDRZTxXmI2ko8JgNpH5h2\nCbPTGeOLSepFaItJRsC1X9/HxshZDYCAP/RWxStoUn6fNixNrdZXIKue7up/TLYSzAAzX5zGHL08\nYVyHJlgEmU2QcFuIVM1cTay55kvoWAWZ8Cr6iWic421hMiZT+QNYaICi508IpqC5I90laSZZx2At\nEJQTYfTJmxMo9ULtejWGXwHwswC2wmd3MvNT+v5pAHfq+3sAfCoc97h+VjUiejeAdwPAFOsvostX\nb+0nH8H8bQ8C0GSoLTm6a8lRqWfXLCgT1vczup2E+WbC7JjkH5g+z1hsErqNMkltl7FUcJXr6yoL\neWjzujpsis0gb2QF6MXX0CLJxn9bXysNJxxnixcpHE6oNJy0EA9FM4cDl5HpZ1oH4qsnSQkdDAj/\n8B7i7l+ZXSjaQKXRYHBPkVnIoX9chIL/DkLfoyAL48LhmRkIaoDtaEfAwvYgq7CRhb1Yl+S0IInV\nMBajYQVmGgAo864tOTlXAsY2N1VQ9VMJq8/jhPGHP4tb0a5JcCKiHwVwnpmvauQwc5Tf19WY+b3M\n/CAzPzjC5NonXGcbf+RhV81s0ONO4XY3le/SnLH2fI+NZ3o0c/H5t/uMtfOMyUUukzkpqGfo92Dn\nKTdXq7CR+OQcCNvZVo3aKu0g7rorzhm620yIVf75POiLXVN3SYtaNEwiugajCVOBsVjhpdBFav2J\n/+L50SPg2k0vv2X/nIxmYxbOMd6I09P1PiNGE8cugr2uYakAtxiS1AHrTzPWz/cY7UiRHOoZs+MN\ndu5qsX86YbzN2Hyyw9pz3bLJYElUzMOg74fxDT7/YlYrSNq+5kDMiVslFIDr0xj+JoAfI6IfBjAF\ncIyIfhPAM0R0jpmfIqJzAM7r8U8AuC+cf69+9rI19wkjqG2DSRi/M2ZemgNrz/VYY0GIFxvCSFt7\nVr7v1gmLjQI8ubvT0Pigqntj1LkZQh+og7tNI3g5XPh2Xvy7ujfUi8130UEuiqHXxM6RyExGM6cq\n8av31ez5YR8JXrhmKBiWxgH19xHcbWbw/AhDDIXiIh5ctwp2sj4PhE51z7oxON/ErqsCbLSjSVP2\nMygDi82Eg+NSoGZ6UYTB0FyohF10OQbhEFmgRmcmqxFhRYfVI4HW6OMvap+94e2aGgMz/zwz38vM\nr4WAip9k5p8E8CEAP6WH/RSA39H3HwLwTiKaENH9AF4P4ObFh65oTgKxh8Co2GOxiWkh0jz17HhE\nWmRML3Y49vUOo12hBTcHjLVn2f3puREAzcq9RdqwM+7ULq+0C52QjpL3IpQqJmZovqgDA28IQPqx\n2i/bAS0k2zNBD0A39wK0AWQb2ueAhzobccq0AVfpg7CJOMZyB8uraQWuZXDoV8YLCgX7LRtbF4jB\nVDH6uHSm3LsJHxMKuVXT4TkxHbq1hN07W+yfShjvSPm96cUOVWq6NnnuReMdVPecQvYoPcYKxERN\ntmA9wnhsZuKufLm9EMN2TcHwAu0XAfwAET0K4Pv1bzDzFwB8EMAXAXwYwE/fbI/Eqjb66MOuNXgB\nUEYp4RbMDVvA1JVS7P6gM2Pt2YVOjox+QphcYqw9w26PG/BWudkQvhuo0u7Oq9SAsvCqrFHhnFVC\nY+XiC81diSngBlx2WyaAW7Gf+2k07oNqr3kfjdC1tCtjhaCy+6UX6KMBfiYggxfBCsBYXMgQJ6Bc\nXMAVZTmMq13PheFAM2PNxdnMxbvQzhg751rsnRGuxdbjHSaX+yXzAFlwqjxKlWZiHgZP+c4owsDm\noNWLANzjYOSmPJK5eauFAgAQ8zVm1svQjtEp/k5660259uL7HyisshCZZruL5R20V5/gVvqLsRTN\nxm3C7ESDvbNJcj/0wP4Z8h3XbHPPoRCTkISJZP2IC83U3Mi480xIcQEMW8Qegjpvu+PV3JV23MFd\nPXjEmD7Vot0Px/pNh7cDjaC6/gu0FxIkUVtYeY+h31X1ayrvIwjqHoemHOd/q7CUsoKiAc63CO0+\nY+N85xtENBeG9wqgMhvsNyVzNYtGkVk8D6uYjIvyKpqLRATfTFzh4/x/PcLMD17Psa8M5uMLtYGv\nuHJjBi2isumCe8nOAQAL66bMGF8WW3T/VIPFFmF6QR7wYlPChykDvQGNTbGDI5o+NAmcfUmFSUkA\nDNr18g1De75BIWXpQhna6yvxi2DeTJ5r0K2xU4uXFmjEMII5EKMPr7bwI+C75HXgch07r9IwzMwY\nkKw8tDlyKlBMBcd1QkKVHMyl8bZ4FfqxnHvs650UyrFQ54YEM4gmTs/ucaCe0Y9l8RtrEV32zcXy\nLcT7i54x4yzkcUJaZPQNVabKrW6veMEw+ujDmL/9TbKguah75kZKWuHHAKA0zwDXse+lZFz5PPUM\nOmCJ0Rgl7J5r0I8J7S6j3QO6NXIbH6zCwai7SqLx2oW26AyIpGAva4vqdsUq1EViu6AtKsuInIa2\nOYWFbzY8hMfhsRFXsemXtIKwGKvrDXZ7d68OhIKdW/E/qOzyxBDKs35uQrMyH+LiI3jkrGuEip/4\nfNgVj1OvBXE3n+4lHb8Kanc1Dvpobu/4WTPPjh2QagZQzZQTnKcAiKBhlOvY8ZQZ82Mt+jFh67c+\ntfTbt6q94gUDAIw//FknP7mNrLuBawMBeLTFH5NjxGAWNhBT+e5N32Pr68J269ca7J3R+IwdWQX9\nlCRVm+ENmvAELTwfZSQ3RRfjSldrmPgFvAo3bIsKRYOIu7Cp3kMvCtm1FQSNhVpi2vRVzRZ0FBBL\nwmBgwkRPgfc7F6G2xDxt6nFYBpLhHgYQPI6hPRDtwMZzcimLK7LnZXPBSEmkafdn2Z9zpSm1VOo7\nuDDQ+WHjDtlsmnkGax1Ka/1Y+BDduniCTv2Lm8r/e9HtVSEYACE/LX7wQdkth3hDMCecaIJgLxIA\ne2/0VRpMUr1ms99j8wlREXfPNvLgsyTqQJZgrr07U6lapU/AVWVTm4NwqEhTjBJ7YLs615iECQQ7\n3zwVQHDxmX0eFt+Sj98EjAF4EeNIWNIsPG2a9e0qAksGMHwXcAIXBHq85UkEijY1xDTsbysHCMAx\nhHZPuAK5IYz3GGvPdzrOxWRIHfuYcSsqvsU3ROzJCXMAWDeLtGA3B5q+BEJBQcekn6V5Rj8Rhtns\neIPZCSmDN7mUsfkvP43D1l41ggGAc9aJAQ72XB7Jg+WmdiVZVJ/tJMRFIJhdGXf0qImkecaxx2Qy\ndFPC3tnGj1s/n9FNxdSwtGSGmFOGs/aqxTLYKSOrEQjvV6nxGLjogFq9R7gGUHkKIvgZd3wjaJlG\nsgrUrKwOW1BBYPl3JnSGpglQA7ehjzGQjBA0GhIBNb7IqjUQJhclzyKgtn9DcLxINwXrY5VOjeWW\nrOq0gdTitcpeWJi6XH1upqrPp8zoJwnz4w1mxwntvrhGKUsU5mFsryrBYPyG7q0PyGR0miqXiDc1\nL5hQkVI8iKoPyTVM47CdIhfV1I6R+HvC+EoPTkKN3T8lWXwsbr+dMRZrhH1Lew5dQLGwDsruzUAV\nAeoajgFu0UQIC9K1jyH9OGoKVC88iufaB1i+fjRN3F4Pmk8EMU2wpSD4Yp/SHF4FLAZK+b0o98C5\nCUnOmV5mxR8IkyuM8RUBAynX3iYxYYoZQMM8ikaO82dLIIggYYuEtErUqhlYMwHBjYb+TxL2T0r8\nyWg3ozmQgL3tv9Lgvv+xruJ+mNqrSjBY6yfJAUUJXjEXk04IKovd0sTJLi7nZNv1jNlm6iPgtmaV\nnUfTgqdFBnPCxjMd+DkgjwgHJxocnJTIvPFlu54wLOOiqZB9wJOaWnPb3uIhginiWEU8P2IOEcsI\nPIKo+kdgc5UGY59XRW/D71rz9PbhN0DBgxKwgWFRFx6F/mtfm/2gKTEwvcAY73TqBiQ3GYYeE+pL\nhGN8boBoAM5z0eeGRMhjxQtIj0fdH3vu/Tjh4GQjgXiatq6fEC6+oVEwlDG5gEPdXpWCYfL7n8X+\nj78ZzQGjn8iDd/tRzQ0BHDU4ps8VUGnx8qYy0lxDZU0g2Hk57EZ2vCf4FI1kcy8jN0WTWGzIb0wu\niR7LiaRykpKThixDIOzQkIjGSk3nYJIEjSMu+Aq/AIrNHzGIqF2gNlts147MSW4AKCdDEr8EoRPM\nBnvN7eB7FAHh922uyk68KCYILVPX+IrlYFNh0IWBCGCyJ35xQaBagT43My195/dK2sHtHU1IBS+7\nDQnGyyPRYADxTu3dJWB0uy/PdXqBsfVbhwtsHLZXpWAAgLV//Rk8/w+/G/1EEo9K7v5cVGdGtZtk\nAkBUxcs7w02vmRay6sxHHVVMBy5DbL7boZmRZozNJ/qixiZCPyXMNxLmmZDHpC4yRnMAjPYZubH4\nDSr1IpIsxPE2S0FbQGIcAiYRXZ8RZGT7LNyTuVvBch2PkRgV4dAbaSgIEDcpkpTgW2omaFZ9ZUKs\nB9pZ+duK67QHEuTU7ueyy5sp0BehDBThbe/dZIjq/6gmJUXwuXp+bcGUhLPSYLYlz6mfoLBLNZ9H\nMxPQeeN8Rrub0SgAOfn9Wxccdb3tVSsYAOD0P/8j7P3d78RsK2HvTMK0JYy3+5KuWxcvwuTjVrYz\n6uQhu80PSIEbwFXPiiNPwXfds6PWQNl1EFRfMKPdY7Q7PdaeT0q5Jsy2GnTrwN6WpUhjjK9wqait\nhVZG+xkHpxq/D2sxmKpayNc7aGFBx53c8QnDJgJAOvzc3ZJz8dJQL8QwGeMiMC3mI82EGzK9ZJwD\nfT46XhUXxJ5bTIwS+QdtQk4CQno/21pLAOB0ZzMD+2kCMaNbS9g/LW7GflyqehkeMrkkMTXTy5LB\nSe5T5goTbguhALzKBQMArP+rT+O5f/I9aOayQ+zemYAkdQ7GO4xmnrViE4NRFi0AD8AySqub761g\nE4ZZyERHYVOm4vZywlWwa0E6qc0U0epN7YLRHHBlN+dWtIl+nLDQ9Pj9McL8eCMl4i0wK9rYQZU3\njcACioBlXKAiMGlzU2IBT4eXOqiQYo11CKXnTBhZ7IN+t1iXsWhmLCSrGaM9yJUAsDEyld3GZyVT\nMNUeATFFSrIecx9C61tYYRzK+rke361JcdxuSlhsSSq6PGINCyekOdDuSYj2eFeqYonml12zaObZ\nr0kvY4bnG9Fe9YIBAF7zTx/C0z/zPZidLjvP7CRAOSHNE8aXhS3X7jNGe5LaXCjVJW+h+8WHwKPZ\nqUljKcxNFunYekwEK13IpIJLAGUHs8+oV81iL2N8WU/ShZO1InJuCfONkoLdvC6OJQAVCcoxiQFj\nscIGUNvZ0lcpzCs7ecAssixIymJnp5CfQLIiBZWF1eWnmpm7hc0jBFnk3o1ATooahDEQReODg4sC\nzNoYalCdlrjrJoTZSaG054mEojOx58YcXyYpM/ecZHpuDrIHUzUzETyWZEXMLXWDk+QJuZ3akWDQ\ndtf/+hD2f/zNmG+Imjg7SV7ZaH7MiEGENJMgo3ZX6ie2M9nFfYFl8WJISTR2kpQRZqjTqDylZANl\nQkfOhPvatXnxVkKlMVQovS8uESy0z2hUEI0vk/ctegOygqaFakxVRWxOUrU6RioCKAlKohbhmpRc\ny0DachP1+4o3oMLO7129Qf3YuABBSDbxpkuMgYHIbNGLQG1G2P2NpIDNfFPS0y82JbJU3L1yreaA\nsPa01I6UiuM9RrvZBWozy1ppXIRCHic9L4vZoUzaPLp1WZheSjsSDKGt/evPIP3Qm5CeychPJC+M\n26/JbiJl0YBuDZidBvbukVliNQbSXMgr7Y5oGOOdjGYhO06m4hIlSxVuO3RYHAwG1BSJqrIlCInC\nwDAN0lR10RQp1aSDhuEBQMUmbrpcCaCIvC+5K2MGIvvtQDSyFs8pFcNlLMWrkF1bikKRWwLl2vyg\nToVZGggHB4ZR3ms9zNySE8sW68DimAj5PGavmWHJZNt9AWunzwpgO94VM8bozZa52cY0Gf6hbMY8\nJjcVrIhyxFNGt6FQAI4Ew1Kb/L/yILu3PABuhdKadnq0e4TpRZ3MrRQjXWwItbmfyq6TW2B2knFw\nh6njCWkh9uh4W+znZiaFdinLjkrMrnoDqBa12dNAWeC2k+bIK7CkIOZzBxe/PNfCoZgyK27eSvtF\nt6Ht6mruxL/tmCGRqVCkdcE3pS+CvSTJg3CQwWQmFYIpFUBYqj045sKVmAjxCHQTQjcldBtSCiC3\nWi/DBZP0l3rC+JIEUjX7iiEtMkZXupry3RDSXJ5RPxHuAlioz+JqZiBZ2LQyHTtGUg0xzTPaT94+\neMKqdiQYrtLM5Wj2v4XRtnPZ2RoCJhc7sX8b0oxJolnMtwjdprjpuGV5vxlBvCIwmpntWqyZiKFa\nhkZ1qsrvi7KHL2AAlT8eKIvQNAxTnwUzKIvevSRhB+YAiorGUnZzb7ogxawoC39IcQarqcTF7ODQ\nT9OaIt05pyAUU3KTx3CSbk3csouNggPklt39mhs1eXpJid/MAeqEOzDaFSHQ7Gc3BSKF21yWQNGM\n8khjHYzuPBeTIY8V59DQ60Y/N2zJqNK3czsSDFdpZhcuvv+Bkl0nTFz3KvQMUhuzOegwDbkfjD3J\nSUCpfiQ282JD3Ir9GOjXgPlx3d1gNR1DiC5kcqeFBN2Yp6E5EBOlWRhSLgs1LWRx9K7W1si95zBQ\ngeILW8FGhixawx3kInqyeTconK+L14FJN3UKk7CfpuImTarWt+Qp5/JIQ9Q1X0KemMCBq+YmVKmT\nvrZ7QLMNjLbFGzLaYzQzuf/UiX1vtHfPwBQ0IY9/WNS5OkhBUsMMKlCZxLTglsCtAIsuONTrcTt5\nHl6oHQmGa7TRxyUqU9yS7G663JIw6ACPrjPb08hN5oZMWdVm29kqwI90YYhK3E/ENOnWbQLLrphH\njDwB5idQbH8GZAHa34RmZl4SuKvS0rNZijZJLqI3yMEm1nszFx5rXgEzKTwdXCqFWx20JFHv+5BF\n29iQ7qJMDDbCVIExwE3hYYjrUu4h7UsuRuqU2DUTF2BjOTrnXGEWQBFYxk+Amj2EIhCaeQYpj6Gf\n6AIPQGoekfMciuZYsoWboOnHSUxNzcZ0u3keXqgdCYbraJbsRQAmWTiRIk1drtyJQJk8RlryHVYT\nvxj/gRtJKVbscF2U5uLUhCK5kR1WSqjDP7ekr/Y+j4qG4IFG0Xugu/rSa1QsEiGWdbPTYoHdSChy\n9cauowvIU7qrKzR1hLRXeA+y0EXzAcv70b6MSzNjD2JLAVytvCHa9zxSNyRRnWgnBr+xCR1d8Aoo\nNnP1ZITz00KiIc18cFMOKiy5foavNKEAHAmG627RtAACGIYgIEz1D6+6kZfYCVVvOSniHklFIbQ3\ndQzkjOYAFYZQUXpDkhHDB9zuN41E8Q+rIG3EIiMLGUMxsiBzW7gYkd5s/fKYBbPJDeCDsBQj/yG6\ncSuswjAOEzBqgnjIsvZ/SFXmNgHqAWhmota7W9RIZSGvp+dTtHGLSVf67NR2AFpdKmlEbBEg3h9m\nGAcFkGu/UkyHYTsSDC+yjT7+iGgPFqBjZKSw8Kt4gb5oA0KpDi66USE1AcXvb6qpXdep2fFvBROB\nepECcOKQ8CVy+WwGv4a3gEG4Ot5cZXHZIo6u0xh/YC0NPlMOBum1hyHrHpHa5XLvuajoVVCaLurG\nUvJ1hXnq1zNhZ7k8e66xFHtuuWgb1LMImgQXDjFgzTgWkTJ9O/ITrrelax9y1IZt/OHPloWSqNIc\nPFrRdmfP7ZD9vRffhUxs24EtfThQFgxC3L+Rn4DyGittcVyQJnBIVOYYdrxqIbbxgoQAAA2vSURB\nVDsb0r5PRbvwhc3mYkW9wExI2T+9ZhReTCUoqcqubPdo0YyqlSRd8JK4N1VjbbkOomuUOlm4uS19\n9r9VKEg17xK/Yqn6LDYmj8XD1MyEuyB4kXk6ZEzyKKG/haXjXq52pDF8g230UbEpu7c84OI1mV3e\nll2dOhbqANULwtDvaL/aQsqt8hF0V7T0Y/IjIXOx0aIZhTwUwqSHHomKpg1UQoZHqSq77vEFusBi\nVCFQfsfp3ObiC/EHVfCWUb4tpiSzV27Ko1QgkFzcg6TZvC1qFRA5FzMkld9RM62Bx6oQUGES0bNk\nNGwDjp1xmsQlKsKCneJsmMX4o68sLOFq7UhjeImt/eQjZUGPU8nL0Isd7G6vVC9+blNxmwGy8waw\nLe6KQFDBdWccNhcCEUwkcn5CjHLkaGfrLk8hZ4RfL2ggtnBM+ORxcg6HtWivRw2nYkV2uRT26a3m\nggCGaS7fibuxsER9LJTPEDUBQH8zCT5gBC4hIck4GwVdhHGNERiNujyDAj4KCJoV6KVXLJ6wqh0J\nhhvQ2k884pPOzYY2eXxEtMO9hgUXUC2CktSFGAoHBwvQCBQgECgL2ROUDGRGCR3WxU1lJ6+wC4vo\nJCqsSbt++F15U84HFMhsqM5XoALKtJsSxVjMLBkQdoHCLTm70NKvu2C1ceqLne+VndpC6vIsW8ZS\nnGelNScXVuYyNtMgalJm0plgzCOhxr/SvA7XakemxA1qVlZs/jYp9GO+dTMjkk7GOKnd5IhuygBY\nAihmBUPiKCKKrwsxzTLIL4yidlt8QviwMmMQWHoWleiCKqTIt35EbcDU8YBLWIATa0IbT1iDAaDZ\nFzXed3fVhgilKItrMoADhXIx63uqYkI836ZFVEKEUA64gWspkathzyAmcNU+jj756hII1o40hhvc\nxh95uKIexx3KQa5WCTzz7LtVVI2BshsC8B3UzICYaiyCip58RZuHdMddPHok7K2Ck6amx+vZq31v\nmbHJ8A9b0AGQNJvdhYstWhTXqKnxVa7NVARjuW/5zngFHvBFKjCpgIpVn5NoX9SxCwUfE9UyTLBQ\nH4BLxRKYcNvHO7yUdqQx3IRmwOT87W9yBh2PyE0FInj9CuoYbDz7rmgRxTevF9XIyxihmNiIOnC1\n3TkSJliULxDpwFXqdd1dne4d3H4xc/aweYZtABYXEU0G/8pJYCGhTVeEBDEkJX84PqZpL67c7DRk\ni10BAehK9GP0+FhUpGke8ZpF+2E3gUjLywGHo6jsrW5HguEmNnNpzd/2YJnkbQH+LAbDSDNVVGLw\nbDgPggwvoELhjbgCFW3EhIIlJQGFcOooSHSHJKASAi40KtNlkDPBTIa2LMDcku/qnvi240qQOC8i\nEWhR6j0CqFV9hptIltk7FgZy3MKwBYLXkwTEROnXkoaZk8c5REFHndK0cSQQYjsSDC9DM+Bq8f0P\niCuNygJwwkxcOLajRTemqtFOrHK1vd59C3aQ/FxbByW6s+6fRUMWlV638JAFachxqHI+DG3zYA64\nQDHiUUx3Z96SQCSyEGZQCXk2r4LfLwG0KCxHA1U9b6OOgcRBaD80EzSAqkx9v5Yw+b1XNifhG2lH\nguFlbF7w5i0PONrucQAhDZi1KrM0olo9UNcHtGoAtRoew4lTSYTii7ENMQi5ZJsC4OZN9AhwDklk\nUo2N2G4f++3RjXo9+80U0u4jZNU2voGlTYs8hjxOBW9R5cSuaULTzLTUF/PM+AomnDgRRh/+7NEC\nuEo7Gpdb0AzU6t76QAj1Zd8tPbuyuuyi3V9V0DI3ZxN3wkJMchU9sBHdDID8BnVcMIJIwAqCBSi7\ncRXbEMLLTROhoUAJ+QxYSU1mXkiW7RLKbJoCAbDEKMYYzcp3MCwmuk4jl4IUO3BQV00O12o6dgzo\nqF29HQmGW9jMpu3e+kDhOXiNA3L114AzaECUk4NU3fbkJpYrESjEIuNAcNBAUBY+hYXuQicwKk04\npVWkqmgOBZPAtAUPU1ZNxMyapGaARzqOk4OFS2nojDJtAKoDnSJYbNFb8wzUw0xYwJFAeBHtSDAc\nghZBr/nbHpQFkMuOGrMyeSSlUokdpTd1Otj0BiBa7AYQcIZgntji9iKtQZhYNKJhDdXur60CH4f4\nifEE9FgDWZnEU2OSyX7bgpe82pee2K81Eqdh3gOGZ2OOdOdmngHVwPpJ8lR9R+3FteviMRDRV4no\nPxDR54joYf3sFBF9jIge1deT4fifJ6IvEdGfE9HbblbnX4lt/JGH0X7yEafjGuvR1HhX8dWGLkVa\nUUKnY9PFSbqzOuinXAtL0w5AXH4G/inF20wPAw+NM+C8hcy1JhKEwjAgzMK+8zgVRqSZByzmRR4n\nZRySe1jyOKHd6x1D6BWfMWFnmEIzkzHK44T2k48cCYWX0F6MxvB3mPm58Pd7AHyCmX+RiN6jf/8c\nEb0RwDsBfAuAuwF8nIjewMyr0o8etas0AyqzahAxijChqPz2XeQnuH0PgME1gKiKACcC2gLYAfDc\njYVjkV3wRArzsGQfUBb/8L1fOwg13+mlgy50jMTk1GzFGaI2ZOZD1IBMeBJeufkRXu72UkyJdwD4\nPn3/PgD/BsDP6ecfYOYZgK8Q0ZcAvBnA4a7ieUjbkKM/f5ulmcNSrgQPhAomRCE2BRck13Z5zDlg\nsRkM9tR1lafEKMeKZ8ToSg7ahR+Tg1mBYiIYE9SEkMc6dIXbYRWtAHg8RDMrFbvyiDD5vSPc4Ga0\n6xUMDNn5ewD/OzO/F8CdzPyUfv80gDv1/T0APhXOfVw/qxoRvRvAuwFgivVvoOuvzuaciB98UBZm\nhi9i9+UrKzD69WOwFoJQcdMhmgYBtIuYRJWAJZdEJ1HIuCckaAKW3SmPUplxJHkuPfBMTRJJyS74\nQG5RwqQ1DBqQEPMjM+HmtusVDN/LzE8Q0VkAHyOiP4tfMjOTR/FcX1Ph8l4AOEanXtS5R61G2Gc/\n9KYq+UtkNloTgBAADGPgJarwkKoc8x4YUEmJCj9Kd3W7PgAB/gIAatmQ+jVNQR21A2ApNoMbAivj\n0nkV6sWwez5CzG9+u64xZuYn9PU8Ef02xDR4hojOMfNTRHQOwHk9/AkA94XT79XPjtpNalfbPWc/\n8iZQB4+dMOKR1cgw3oB7AAJO4Tu+vveoRaCEi6MIDwlf5hJzQeR1JfpJkgjQXPpSMT7NZ6o5GY5w\nglvfrikYiGgDQGLmbX3/gwD+KYAPAfgpAL+or7+jp3wIwPuJ6Jch4OPrAXzmJvT9qF2jDam+xpfo\nJ5IroiI2qZlgLtI8KcFOgKr0xnIkCE8glViJxtKtt4WLAK7jEmKthuykq+V+HrVb365HY7gTwG+T\nSPUWwPuZ+cNE9FkAHySidwH4GoCfAABm/gIRfRDAFwF0AH76yCNxONqqIKH52x4siVlC2ji4JyO4\nFbksfI9wBPxzr2cRciSYKWIJUV7puRJfKY2Y+dpH3exOED0LYBfAc9c69hC0O3DUzxvdbpe+3i79\nBFb39a8w85nrOflQCAYAIKKHmfnBW92Pa7Wjft74drv09XbpJ/DS+3qUwemoHbWjttSOBMNRO2pH\nbakdJsHw3lvdgetsR/288e126evt0k/gJfb10GAMR+2oHbXD0w6TxnDUjtpROyTtlgsGInq7hmd/\nSaM0b3V/fp2IzhPR58Nnhy7EnIjuI6I/IKIvEtEXiOhnDmNfiWhKRJ8hoj/Wfv4Ph7Gf4bcbIvr3\nRPS7h7yfNzcVAjPfsn8AGgB/CeCbAIwB/DGAN97iPv0tAN8B4PPhs38G4D36/j0A/id9/0bt8wTA\n/XovzcvUz3MAvkPfbwH4C+3PoeorJHJjU9+PAHwawHcdtn6G/v4jAO8H8LuH9dnr738VwB2Dz25Y\nX2+1xvBmAF9i5i8z8xzAByBh27esMfMfArgw+PgdkNBy6OuPh88/wMwzZv4KAAsxfzn6+RQz/zt9\nvw3gTyFRrIeqryxtR/8c6T8+bP0EACK6F8CPAPjn4eND188XaDesr7daMNwD4LHw98oQ7UPQXijE\n/Jb3n4heC+BvQHbjQ9dXVc8/Bwm0+xgzH8p+AvgVAD8LIISqHsp+AiUVwiOawgC4gX09imB9kY35\nxYeY38xGRJsA/m8A/z0zXyGLVMTh6StLrMy3E9EJSNzNtw6+v+X9JKIfBXCemR8hou9bdcxh6Gdo\nNzwVQmy3WmO4XUK0n9HQchymEHMiGkGEwv/BzP/qMPcVAJj5EoA/APD2Q9jPvwngx4joqxCT9i1E\n9JuHsJ8A6lQIAKpUCDeir7daMHwWwOuJ6H4iGkNyRX7oFvdpVbMQc2A5xPydRDQhovvxMoaYk6gG\nvwbgT5n5lw9rX4nojGoKIKI1AD8A4M8OWz+Z+eeZ+V5mfi1kHn6SmX/ysPUTkFQIRLRl7yGpED5/\nQ/v6cqGoL4Cu/jAEUf9LAP/4EPTn/wTwFIAFxBZ7F4DTAD4B4FEAHwdwKhz/j7Xvfw7gh17Gfn4v\nxM78EwCf038/fNj6CuCvA/j32s/PA/gn+vmh6uegz9+H4pU4dP2EePH+WP99wdbNjezrEfPxqB21\no7bUbrUpcdSO2lE7hO1IMBy1o3bUltqRYDhqR+2oLbUjwXDUjtpRW2pHguGoHbWjttSOBMNRO2pH\nbakdCYajdtSO2lI7EgxH7agdtaX2/wPL9e5duJNW2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a96bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's look at one of the patients\n",
    "\n",
    "first_patient = load_scan(patients[0])\n",
    "first_patient_pixels = get_pixels_hu(first_patient)\n",
    "plt.hist(first_patient_pixels.flatten(), bins=80, color='c')\n",
    "plt.xlabel(\"Hounsfield Units (HU)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "\n",
    "import scipy\n",
    "# Show some slice in the middle\n",
    "#data=scipy.ndimage.interpolation.zoom(first_patient_pixels[41],[200,200])\n",
    "plt.figure()\n",
    "plt.imshow(first_patient_pixels[42])\n",
    "plt.annotate('', xy=(317, 367), xycoords='data',\n",
    "             xytext=(0.5, 0.5), textcoords='figure fraction',\n",
    "             arrowprops=dict(arrowstyle=\"->\"))\n",
    "#plt.savefig(\"images/test.png\",dpi=300)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "59c11a48-c6d0-8522-f5cd-b03d8ee812f6",
    "_uuid": "4e0fd5da6c5209dacdfdc55ceeb8e5901630569b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def processimage(img):\n",
    "    #function sourced from https://www.kaggle.com/c/data-science-bowl-2017#tutorial\n",
    "    #Standardize the pixel values\n",
    "    mean = np.mean(img)\n",
    "    std = np.std(img)\n",
    "    img = img-mean\n",
    "    img = img/std\n",
    "    #plt.hist(img.flatten(),bins=200)\n",
    "    #plt.show()\n",
    "    #print(thresh_img[366][280:450])\n",
    "    middle = img[100:400,100:400] \n",
    "    mean = np.mean(middle)  \n",
    "    max = np.max(img)\n",
    "    min = np.min(img)\n",
    "    #move the underflow bins\n",
    "    img[img==max]=mean\n",
    "    img[img==min]=mean\n",
    "    kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))\n",
    "    centers = sorted(kmeans.cluster_centers_.flatten())\n",
    "    threshold = np.mean(centers)\n",
    "    thresh_img = np.where(img<threshold,1.0,0.0)  # threshold the image\n",
    "    eroded = morphology.erosion(thresh_img,np.ones([4,4]))\n",
    "    dilation = morphology.dilation(eroded,np.ones([10,10]))\n",
    "    labels = measure.label(dilation)\n",
    "    label_vals = np.unique(labels)\n",
    "    #plt.imshow(labels)\n",
    "    #plt.show()\n",
    "    labels = measure.label(dilation)\n",
    "    label_vals = np.unique(labels)\n",
    "    regions = measure.regionprops(labels)\n",
    "    good_labels = []\n",
    "    for prop in regions:\n",
    "        B = prop.bbox\n",
    "        if B[2]-B[0]<475 and B[3]-B[1]<475 and B[0]>40 and B[2]<472:\n",
    "            good_labels.append(prop.label)\n",
    "    mask = np.ndarray([512,512],dtype=np.int8)\n",
    "    mask[:] = 0\n",
    "    #\n",
    "    #  The mask here is the mask for the lungs--not the nodes\n",
    "    #  After just the lungs are left, we do another large dilation\n",
    "    #  in order to fill in and out the lung mask \n",
    "    #\n",
    "    for N in good_labels:\n",
    "        mask = mask + np.where(labels==N,1,0)\n",
    "    mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation\n",
    "    return mask*img\n",
    "\n",
    "def nodule_coordinates(nodulelocations,meta):\n",
    "    slices=nodulelocations[\"slice no.\"][nodulelocations.index[nodulelocations[\"case\"]==int(meta[\"Patient Id\"][-4:])]]\n",
    "    xlocs=nodulelocations[\"x loc.\"][nodulelocations.index[nodulelocations[\"case\"]==int(meta[\"Patient Id\"][-4:])]]\n",
    "    ylocs=nodulelocations[\"y loc.\"][nodulelocations.index[nodulelocations[\"case\"]==int(meta[\"Patient Id\"][-4:])]]\n",
    "    nodulecoord=[]\n",
    "    for i in range(len(slices)):\n",
    "        nodulecoord.append([slices.values[i]-1,xlocs.values[i]-1,ylocs.values[i]-1])\n",
    "    return nodulecoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate and save nodule images for all samples\n",
    "'''\n",
    "for i in range(15,len(patients)):\n",
    "    print(i)\n",
    "    first_patient = load_scan(patients[i])\n",
    "    first_patient_pixels = get_pixels_hu(first_patient)\n",
    "    nodcord=nodule_coordinates(nodulelocations,meta.loc[i])\n",
    "    for j in range(len(nodcord)):\n",
    "        #plt.imsave(\"images/\"+meta['Patient Id'].loc[i]+\"slice\"+str(slice)+\".png\",first_patient_pixels[slice])\n",
    "        plt.figure()\n",
    "        plt.imshow(first_patient_pixels[nodcord[j][0]])\n",
    "        plt.annotate('', xy=(nodcord[j][1], nodcord[j][2]), xycoords='data',\n",
    "             xytext=(0.5, 0.5), textcoords='figure fraction',\n",
    "             arrowprops=dict(arrowstyle=\"->\"))\n",
    "        plt.savefig(\"images/\"+meta['Patient Id'].loc[i]+\"slice\"+str(nodcord[j])+\".png\",dpi=300)\n",
    "        plt.close()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing patient# 0 ETA: 13.88888888888889 hrs\n",
      "Processing patient# 1 ETA: 2.636670792367723 hrs\n",
      "Processing patient# 2 ETA: 3.67324132439163 hrs\n",
      "Processing patient# 3 ETA: 5.957215151256985 hrs\n",
      "Processing patient# 4 ETA: 5.327770197590192 hrs\n",
      "Processing patient# 5 ETA: 5.793750263902877 hrs\n",
      "Processing patient# 6 ETA: 6.373408978614542 hrs\n",
      "Processing patient# 7 ETA: 6.239070732442159 hrs\n",
      "Processing patient# 8 ETA: 6.071023768178291 hrs\n",
      "Processing patient# 9 ETA: 6.182566953235202 hrs\n",
      "Processing patient# 10 ETA: 6.841921355532275 hrs\n",
      "Processing patient# 11 ETA: 8.01078399029645 hrs\n",
      "Processing patient# 12 ETA: 9.588434534768263 hrs\n",
      "Processing patient# 13 ETA: 9.397183720331928 hrs\n",
      "Processing patient# 14 ETA: 8.963372415550172 hrs\n",
      "Processing patient# 15 ETA: 8.756867530372407 hrs\n",
      "Processing patient# 16 ETA: 9.19578843102687 hrs\n",
      "Processing patient# 17 ETA: 9.062609851749894 hrs\n",
      "Processing patient# 18 ETA: 9.139445660809676 hrs\n",
      "Processing patient# 19 ETA: 9.095597724126794 hrs\n",
      "Processing patient# 20 ETA: 9.123473400817975 hrs\n",
      "Processing patient# 21 ETA: 9.164804511789292 hrs\n",
      "Processing patient# 22 ETA: 8.929756330630996 hrs\n",
      "Processing patient# 23 ETA: 8.716978218146568 hrs\n",
      "Processing patient# 24 ETA: 8.781162216812373 hrs\n",
      "Processing patient# 25 ETA: 8.713223342217338 hrs\n",
      "Processing patient# 26 ETA: 8.773954009953728 hrs\n",
      "Processing patient# 27 ETA: 9.086540090596236 hrs\n",
      "Processing patient# 28 ETA: 8.753199710313762 hrs\n",
      "Processing patient# 29 ETA: 8.708800143601794 hrs\n",
      "Processing patient# 30 ETA: 8.603558787776365 hrs\n",
      "Processing patient# 31 ETA: 8.726033651542494 hrs\n",
      "Processing patient# 32 ETA: 8.444797380879107 hrs\n",
      "Processing patient# 33 ETA: 8.380945623619388 hrs\n",
      "Processing patient# 34 ETA: 8.33558594246121 hrs\n",
      "Processing patient# 35 ETA: 8.212101783139364 hrs\n",
      "Processing patient# 36 ETA: 8.300332735843128 hrs\n",
      "Processing patient# 37 ETA: 8.25922375126286 hrs\n",
      "Processing patient# 38 ETA: 8.128183018981714 hrs\n",
      "Processing patient# 39 ETA: 8.523318658924511 hrs\n",
      "Processing patient# 40 ETA: 8.506017835602163 hrs\n",
      "Processing patient# 41 ETA: 8.505169284505248 hrs\n",
      "Processing patient# 42 ETA: 8.807552373243702 hrs\n",
      "Processing patient# 43 ETA: 8.76621825831189 hrs\n",
      "Processing patient# 44 ETA: 8.834178177204095 hrs\n",
      "Processing patient# 45 ETA: 9.272588361740112 hrs\n",
      "Processing patient# 46 ETA: 9.434555155770214 hrs\n",
      "Processing patient# 47 ETA: 9.305274428332106 hrs\n",
      "Processing patient# 48 ETA: 9.45358432509833 hrs\n",
      "Processing patient# 49 ETA: 9.948135394909484 hrs\n",
      "Processing patient# 50 ETA: 9.803329818603729 hrs\n",
      "Processing patient# 51 ETA: 9.763577398568197 hrs\n",
      "Processing patient# 52 ETA: 9.79616602352287 hrs\n",
      "Processing patient# 53 ETA: 9.70138086154026 hrs\n",
      "Processing patient# 54 ETA: 9.583765381707085 hrs\n",
      "Processing patient# 55 ETA: 9.70688255504165 hrs\n",
      "Processing patient# 56 ETA: 9.740606743851824 hrs\n",
      "Processing patient# 57 ETA: 9.662218144065456 hrs\n",
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      "Processing patient# 743 ETA: 2.9711290542946034 hrs\n",
      "Processing patient# 744 ETA: 2.960956997060647 hrs\n",
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      "Processing patient# 746 ETA: 2.9412901825548134 hrs\n",
      "Processing patient# 747 ETA: 2.93369852081839 hrs\n",
      "Processing patient# 748 ETA: 2.9239921562587003 hrs\n",
      "Processing patient# 749 ETA: 2.910279006994438 hrs\n",
      "Processing patient# 750 ETA: 2.8994284533739076 hrs\n",
      "Processing patient# 751 ETA: 2.88803960198851 hrs\n",
      "Processing patient# 752 ETA: 2.874105736589936 hrs\n",
      "Processing patient# 753 ETA: 2.859457779158634 hrs\n",
      "Processing patient# 754 ETA: 2.8448486742521326 hrs\n",
      "Processing patient# 755 ETA: 2.8385101459910542 hrs\n",
      "Processing patient# 756 ETA: 2.8296471574051045 hrs\n",
      "Processing patient# 757 ETA: 2.8310091429968938 hrs\n",
      "Processing patient# 758 ETA: 2.8182236583394156 hrs\n",
      "Processing patient# 759 ETA: 2.8135769945841176 hrs\n",
      "Processing patient# 760 ETA: 2.7989841194050857 hrs\n",
      "Processing patient# 761 ETA: 2.7901587119027984 hrs\n",
      "Processing patient# 762 ETA: 2.775612610926621 hrs\n",
      "Processing patient# 763 ETA: 2.7650294328603904 hrs\n",
      "Processing patient# 764 ETA: 2.752332524022196 hrs\n",
      "Processing patient# 765 ETA: 2.7473892200881345 hrs\n",
      "Processing patient# 766 ETA: 2.7359912275478626 hrs\n",
      "Processing patient# 767 ETA: 2.7232334074310227 hrs\n",
      "Processing patient# 768 ETA: 2.71614275791139 hrs\n",
      "Processing patient# 769 ETA: 2.716619941804161 hrs\n",
      "Processing patient# 770 ETA: 2.7049009452430375 hrs\n",
      "Processing patient# 771 ETA: 2.6951570075881763 hrs\n",
      "Processing patient# 772 ETA: 2.6807243167007195 hrs\n",
      "Processing patient# 773 ETA: 2.679324279180354 hrs\n",
      "Processing patient# 774 ETA: 2.6672680241146756 hrs\n",
      "Processing patient# 775 ETA: 2.6604491481936545 hrs\n",
      "Processing patient# 776 ETA: 2.651510707704353 hrs\n",
      "Processing patient# 777 ETA: 2.6447610528824 hrs\n",
      "Processing patient# 778 ETA: 2.634441056649062 hrs\n",
      "Processing patient# 779 ETA: 2.6239055644865643 hrs\n",
      "Processing patient# 780 ETA: 2.6181730361606323 hrs\n",
      "Processing patient# 781 ETA: 2.603787871088882 hrs\n",
      "Processing patient# 782 ETA: 2.596643502104093 hrs\n",
      "Processing patient# 783 ETA: 2.586442399630907 hrs\n",
      "Processing patient# 784 ETA: 2.5746038352292615 hrs\n",
      "Processing patient# 785 ETA: 2.5612134569354366 hrs\n",
      "Processing patient# 786 ETA: 2.5549733996719906 hrs\n",
      "Processing patient# 787 ETA: 2.5429918259327535 hrs\n",
      "Processing patient# 788 ETA: 2.528722385276746 hrs\n",
      "Processing patient# 789 ETA: 2.514489114164652 hrs\n",
      "Processing patient# 790 ETA: 2.5027501448747476 hrs\n",
      "Processing patient# 791 ETA: 2.490799529060484 hrs\n",
      "Processing patient# 792 ETA: 2.4781100952038244 hrs\n",
      "Processing patient# 793 ETA: 2.4639853370952998 hrs\n",
      "Processing patient# 794 ETA: 2.451727054844608 hrs\n",
      "Processing patient# 795 ETA: 2.4442657138451596 hrs\n",
      "Processing patient# 796 ETA: 2.430198885658928 hrs\n",
      "Processing patient# 797 ETA: 2.417027790204395 hrs\n",
      "Processing patient# 798 ETA: 2.4110074213607753 hrs\n",
      "Processing patient# 799 ETA: 2.3969946541291836 hrs\n",
      "Processing patient# 800 ETA: 2.3887640541324173 hrs\n",
      "Processing patient# 801 ETA: 2.3793089725908714 hrs\n",
      "Processing patient# 802 ETA: 2.366337386788954 hrs\n",
      "Processing patient# 803 ETA: 2.3552915497350377 hrs\n",
      "Processing patient# 804 ETA: 2.3476167397223295 hrs\n",
      "Processing patient# 805 ETA: 2.340667354003902 hrs\n",
      "Processing patient# 806 ETA: 2.330661393124881 hrs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing patient# 807 ETA: 2.3180904248488536 hrs\n",
      "Processing patient# 808 ETA: 2.316291924637258 hrs\n",
      "Processing patient# 809 ETA: 2.306327914953264 hrs\n",
      "Processing patient# 810 ETA: 2.2937364133493388 hrs\n",
      "Processing patient# 811 ETA: 2.286457296569701 hrs\n",
      "Processing patient# 812 ETA: 2.2725560346806906 hrs\n",
      "Processing patient# 813 ETA: 2.2617019531706317 hrs\n",
      "Processing patient# 814 ETA: 2.251034232953144 hrs\n",
      "Processing patient# 815 ETA: 2.2417898317603897 hrs\n",
      "Processing patient# 816 ETA: 2.2294368812746184 hrs\n",
      "Processing patient# 817 ETA: 2.219648920466718 hrs\n",
      "Processing patient# 818 ETA: 2.205850871338757 hrs\n",
      "Processing patient# 819 ETA: 2.195107054755509 hrs\n",
      "Processing patient# 820 ETA: 2.1879454139882952 hrs\n",
      "Processing patient# 821 ETA: 2.175421501018146 hrs\n",
      "Processing patient# 822 ETA: 2.170169224909718 hrs\n",
      "Processing patient# 823 ETA: 2.157458074163197 hrs\n",
      "Processing patient# 824 ETA: 2.1476923447520253 hrs\n",
      "Processing patient# 825 ETA: 2.1348969293151234 hrs\n",
      "Processing patient# 826 ETA: 2.1283025555226454 hrs\n",
      "Processing patient# 827 ETA: 2.1145997537811247 hrs\n",
      "Processing patient# 828 ETA: 2.1062926469505694 hrs\n",
      "Processing patient# 829 ETA: 2.094295404055469 hrs\n",
      "Processing patient# 830 ETA: 2.0836191664824057 hrs\n",
      "Processing patient# 831 ETA: 2.0740148964068816 hrs\n",
      "Processing patient# 832 ETA: 2.0654819308808006 hrs\n",
      "Processing patient# 833 ETA: 2.0564778292126618 hrs\n",
      "Processing patient# 834 ETA: 2.0493494363042193 hrs\n",
      "Processing patient# 835 ETA: 2.039825969443508 hrs\n",
      "Processing patient# 836 ETA: 2.0290846199297103 hrs\n",
      "Processing patient# 837 ETA: 2.01546348579753 hrs\n",
      "Processing patient# 838 ETA: 2.005447883919312 hrs\n",
      "Processing patient# 839 ETA: 1.9933035449275385 hrs\n",
      "Processing patient# 840 ETA: 1.9812912975719983 hrs\n",
      "Processing patient# 841 ETA: 1.9753400090185198 hrs\n",
      "Processing patient# 842 ETA: 1.9638249546655202 hrs\n",
      "Processing patient# 843 ETA: 1.9514639384197876 hrs\n",
      "Processing patient# 844 ETA: 1.937949926474727 hrs\n",
      "Processing patient# 845 ETA: 1.9281037407843076 hrs\n",
      "Processing patient# 846 ETA: 1.9190993229329736 hrs\n",
      "Processing patient# 847 ETA: 1.9061921579426822 hrs\n",
      "Processing patient# 848 ETA: 1.897727656750089 hrs\n",
      "Processing patient# 849 ETA: 1.8880667505575062 hrs\n",
      "Processing patient# 850 ETA: 1.8770137554382968 hrs\n",
      "Processing patient# 851 ETA: 1.8682536056029575 hrs\n",
      "Processing patient# 852 ETA: 1.8558800645825553 hrs\n",
      "Processing patient# 853 ETA: 1.8479423173181688 hrs\n",
      "Processing patient# 854 ETA: 1.834523809828966 hrs\n",
      "Processing patient# 855 ETA: 1.8222395945473726 hrs\n",
      "Processing patient# 856 ETA: 1.8104207574260465 hrs\n",
      "Processing patient# 857 ETA: 1.7987228732836735 hrs\n",
      "Processing patient# 858 ETA: 1.789100652370188 hrs\n",
      "Processing patient# 859 ETA: 1.777950811450717 hrs\n",
      "Processing patient# 860 ETA: 1.7741327617209228 hrs\n",
      "Processing patient# 861 ETA: 1.7637005062228814 hrs\n",
      "Processing patient# 862 ETA: 1.753690597628942 hrs\n",
      "Processing patient# 863 ETA: 1.744196951749996 hrs\n",
      "Processing patient# 864 ETA: 1.7446075173163849 hrs\n",
      "Processing patient# 865 ETA: 1.7345443637488966 hrs\n",
      "Processing patient# 866 ETA: 1.7229643270377164 hrs\n",
      "Processing patient# 867 ETA: 1.710528968916816 hrs\n",
      "Processing patient# 868 ETA: 1.698456728216384 hrs\n",
      "Processing patient# 869 ETA: 1.6879593382298137 hrs\n",
      "Processing patient# 870 ETA: 1.6755701560953578 hrs\n",
      "Processing patient# 871 ETA: 1.664889187809915 hrs\n",
      "Processing patient# 872 ETA: 1.6538566858423192 hrs\n",
      "Processing patient# 873 ETA: 1.6412230363935385 hrs\n",
      "Processing patient# 874 ETA: 1.6293372688358168 hrs\n",
      "Processing patient# 875 ETA: 1.6165276867630363 hrs\n",
      "Processing patient# 876 ETA: 1.6044321183374934 hrs\n",
      "Processing patient# 877 ETA: 1.5917258635002831 hrs\n",
      "Processing patient# 878 ETA: 1.5797360400493097 hrs\n",
      "Processing patient# 879 ETA: 1.5665868960120335 hrs\n",
      "Processing patient# 880 ETA: 1.556935818351202 hrs\n",
      "Processing patient# 881 ETA: 1.5452628296743731 hrs\n",
      "Processing patient# 882 ETA: 1.534062700845352 hrs\n",
      "Processing patient# 883 ETA: 1.5273584722689582 hrs\n",
      "Processing patient# 884 ETA: 1.5142454711247066 hrs\n",
      "Processing patient# 885 ETA: 1.5011621026058877 hrs\n",
      "Processing patient# 886 ETA: 1.4881082664061251 hrs\n",
      "Processing patient# 887 ETA: 1.4750838626316585 hrs\n",
      "Processing patient# 888 ETA: 1.4630588895729084 hrs\n",
      "Processing patient# 889 ETA: 1.450679409550125 hrs\n",
      "Processing patient# 890 ETA: 1.438400992488221 hrs\n",
      "Processing patient# 891 ETA: 1.4265461897274712 hrs\n",
      "Processing patient# 892 ETA: 1.4136378979936832 hrs\n",
      "Processing patient# 893 ETA: 1.4030989810532568 hrs\n",
      "Processing patient# 894 ETA: 1.39177392817717 hrs\n",
      "Processing patient# 895 ETA: 1.3797340155771094 hrs\n",
      "Processing patient# 896 ETA: 1.3687283580883276 hrs\n",
      "Processing patient# 897 ETA: 1.3610914508634575 hrs\n",
      "Processing patient# 898 ETA: 1.3519448976547979 hrs\n",
      "Processing patient# 899 ETA: 1.3406462999016973 hrs\n",
      "Processing patient# 900 ETA: 1.3282397487428452 hrs\n",
      "Processing patient# 901 ETA: 1.315425795448858 hrs\n",
      "Processing patient# 902 ETA: 1.307663963225916 hrs\n",
      "Processing patient# 903 ETA: 1.2962642768059494 hrs\n",
      "Processing patient# 904 ETA: 1.2834722645746337 hrs\n",
      "Processing patient# 905 ETA: 1.2716347566666668 hrs\n",
      "Processing patient# 906 ETA: 1.259271020848931 hrs\n",
      "Processing patient# 907 ETA: 1.2472313063081593 hrs\n",
      "Processing patient# 908 ETA: 1.237394071814991 hrs\n",
      "Processing patient# 909 ETA: 1.2246931507406447 hrs\n",
      "Processing patient# 910 ETA: 1.2135186849339112 hrs\n",
      "Processing patient# 911 ETA: 1.2020535815570514 hrs\n",
      "Processing patient# 912 ETA: 1.1900829400104744 hrs\n",
      "Processing patient# 913 ETA: 1.1774578079626594 hrs\n",
      "Processing patient# 914 ETA: 1.1654297070223685 hrs\n",
      "Processing patient# 915 ETA: 1.1559700630588405 hrs\n",
      "Processing patient# 916 ETA: 1.1455256752792735 hrs\n",
      "Processing patient# 917 ETA: 1.1329470557992014 hrs\n",
      "Processing patient# 918 ETA: 1.1221031839006093 hrs\n",
      "Processing patient# 919 ETA: 1.110407747569158 hrs\n",
      "Processing patient# 920 ETA: 1.0990909054651496 hrs\n",
      "Processing patient# 921 ETA: 1.0865790941125892 hrs\n",
      "Processing patient# 922 ETA: 1.0765005560973866 hrs\n",
      "Processing patient# 923 ETA: 1.0643278972583377 hrs\n",
      "Processing patient# 924 ETA: 1.0529928972992457 hrs\n",
      "Processing patient# 925 ETA: 1.0408684916221338 hrs\n",
      "Processing patient# 926 ETA: 1.030209115970069 hrs\n",
      "Processing patient# 927 ETA: 1.01840183069405 hrs\n",
      "Processing patient# 928 ETA: 1.0065156731481935 hrs\n",
      "Processing patient# 929 ETA: 0.9954391341636579 hrs\n",
      "Processing patient# 930 ETA: 0.9847513505402014 hrs\n",
      "Processing patient# 931 ETA: 0.9731169205842121 hrs\n",
      "Processing patient# 932 ETA: 0.9611886767731792 hrs\n",
      "Processing patient# 933 ETA: 0.9491434733226237 hrs\n",
      "Processing patient# 934 ETA: 0.9368400798427784 hrs\n",
      "Processing patient# 935 ETA: 0.9255338299611584 hrs\n",
      "Processing patient# 936 ETA: 0.9140406214789699 hrs\n",
      "Processing patient# 937 ETA: 0.9017927867555106 hrs\n",
      "Processing patient# 938 ETA: 0.8908903338226547 hrs\n",
      "Processing patient# 939 ETA: 0.8790499859165963 hrs\n",
      "Processing patient# 940 ETA: 0.867645670229168 hrs\n",
      "Processing patient# 941 ETA: 0.8571213235079793 hrs\n",
      "Processing patient# 942 ETA: 0.8457575261867047 hrs\n",
      "Processing patient# 943 ETA: 0.8338529387366785 hrs\n",
      "Processing patient# 944 ETA: 0.8217133166986867 hrs\n",
      "Processing patient# 945 ETA: 0.8095993858014461 hrs\n",
      "Processing patient# 946 ETA: 0.7975110645771787 hrs\n",
      "Processing patient# 947 ETA: 0.7858112282418975 hrs\n",
      "Processing patient# 948 ETA: 0.7743810826747479 hrs\n",
      "Processing patient# 949 ETA: 0.7636945285008248 hrs\n",
      "Processing patient# 950 ETA: 0.7526866500427847 hrs\n",
      "Processing patient# 951 ETA: 0.7408978711493264 hrs\n",
      "Processing patient# 952 ETA: 0.7291934508826833 hrs\n",
      "Processing patient# 953 ETA: 0.717613753560846 hrs\n",
      "Processing patient# 954 ETA: 0.7058432728658186 hrs\n",
      "Processing patient# 955 ETA: 0.6987053400374478 hrs\n",
      "Processing patient# 956 ETA: 0.6928846353811342 hrs\n",
      "Processing patient# 957 ETA: 0.6810718704415962 hrs\n",
      "Processing patient# 958 ETA: 0.6694656042223384 hrs\n",
      "Processing patient# 959 ETA: 0.6595618164232716 hrs\n",
      "Processing patient# 960 ETA: 0.6477108281881859 hrs\n",
      "Processing patient# 961 ETA: 0.6356853566901353 hrs\n",
      "Processing patient# 962 ETA: 0.6243337347955498 hrs\n",
      "Processing patient# 963 ETA: 0.612603263201371 hrs\n",
      "Processing patient# 964 ETA: 0.6011416018090252 hrs\n",
      "Processing patient# 965 ETA: 0.5893107740026688 hrs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing patient# 966 ETA: 0.5782392379871878 hrs\n",
      "Processing patient# 967 ETA: 0.5664571811793172 hrs\n",
      "Processing patient# 968 ETA: 0.5550505765623954 hrs\n",
      "Processing patient# 969 ETA: 0.5433182724472762 hrs\n",
      "Processing patient# 970 ETA: 0.5316573555100149 hrs\n",
      "Processing patient# 971 ETA: 0.5205682803516679 hrs\n",
      "Processing patient# 972 ETA: 0.508727682949094 hrs\n",
      "Processing patient# 973 ETA: 0.4980411447653205 hrs\n",
      "Processing patient# 974 ETA: 0.4870250405290608 hrs\n",
      "Processing patient# 975 ETA: 0.4752110044446757 hrs\n",
      "Processing patient# 976 ETA: 0.46504625133118277 hrs\n",
      "Processing patient# 977 ETA: 0.4539306261454577 hrs\n",
      "Processing patient# 978 ETA: 0.4434611245786639 hrs\n",
      "Processing patient# 979 ETA: 0.4318696981290325 hrs\n",
      "Processing patient# 980 ETA: 0.4210811269784063 hrs\n",
      "Processing patient# 981 ETA: 0.40988899986313804 hrs\n",
      "Processing patient# 982 ETA: 0.3980974162817256 hrs\n",
      "Processing patient# 983 ETA: 0.3863963788954627 hrs\n",
      "Processing patient# 984 ETA: 0.37465066828903826 hrs\n",
      "Processing patient# 985 ETA: 0.3631752270280615 hrs\n",
      "Processing patient# 986 ETA: 0.3524106742597158 hrs\n",
      "Processing patient# 987 ETA: 0.34108943489909505 hrs\n",
      "Processing patient# 988 ETA: 0.3293860870505907 hrs\n",
      "Processing patient# 989 ETA: 0.318196762893832 hrs\n",
      "Processing patient# 990 ETA: 0.30668428376046 hrs\n",
      "Processing patient# 991 ETA: 0.29526219972621526 hrs\n",
      "Processing patient# 992 ETA: 0.2841154608231267 hrs\n",
      "Processing patient# 993 ETA: 0.2728992208917488 hrs\n",
      "Processing patient# 994 ETA: 0.26126532581590556 hrs\n",
      "Processing patient# 995 ETA: 0.24965481433543674 hrs\n",
      "Processing patient# 996 ETA: 0.2388495031098925 hrs\n",
      "Processing patient# 997 ETA: 0.22764489951240546 hrs\n",
      "Processing patient# 998 ETA: 0.21609847485388753 hrs\n",
      "Processing patient# 999 ETA: 0.2045199511251647 hrs\n",
      "Processing patient# 1000 ETA: 0.19352955870158137 hrs\n",
      "Processing patient# 1001 ETA: 0.1821247825755927 hrs\n",
      "Processing patient# 1002 ETA: 0.17060915948209487 hrs\n",
      "Processing patient# 1003 ETA: 0.1593062786302168 hrs\n",
      "Processing patient# 1004 ETA: 0.14777992801474385 hrs\n",
      "Processing patient# 1005 ETA: 0.13640223957175798 hrs\n",
      "Processing patient# 1006 ETA: 0.1249908159530686 hrs\n",
      "Processing patient# 1007 ETA: 0.11354451434614626 hrs\n",
      "Processing patient# 1008 ETA: 0.10232848926314343 hrs\n",
      "Processing patient# 1009 ETA: 0.09153430935360019 hrs\n",
      "Processing patient# 1010 ETA: 0.0802527884914404 hrs\n",
      "Processing patient# 1011 ETA: 0.06891869134648408 hrs\n",
      "Processing patient# 1012 ETA: 0.0574127095984194 hrs\n",
      "Processing patient# 1013 ETA: 0.04591412992190776 hrs\n",
      "Processing patient# 1014 ETA: 0.03444034594324775 hrs\n",
      "Processing patient# 1015 ETA: 0.02294412677889745 hrs\n",
      "Processing patient# 1016 ETA: 0.011465803991060158 hrs\n"
     ]
    }
   ],
   "source": [
    "noduleimages=np.ndarray([len(nodulelocations)*3,512,512],dtype=np.float32)\n",
    "nodulemasks=np.ndarray([len(nodulelocations)*3,512,512],dtype=np.bool)\n",
    "nodulemaskscircle=np.ndarray([len(nodulelocations)*3,512,512],dtype=np.bool)\n",
    "index=0\n",
    "totaltime=50000\n",
    "start_time=time.time()\n",
    "elapsed_time=0\n",
    "nodulemeanhu=[]\n",
    "nonnodulemeanhu=[]\n",
    "thresh=-500\n",
    "for i in range(len(patients)):\n",
    "    print(\"Processing patient#\",i,\"ETA:\",(totaltime-elapsed_time)/3600,\"hrs\")\n",
    "    coord=nodule_coordinates(nodulelocations,meta.iloc[i])\n",
    "    if len(coord)>0:\n",
    "        patient=load_scan(patients[i])\n",
    "        patient_pix=get_pixels_hu(patient)\n",
    "        radius=nodulelocations[\"eq. diam.\"][nodulelocations.index[nodulelocations[\"case\"]==int(meta[\"Patient Id\"][i][-4:])]]\n",
    "        nodulemask=np.ndarray([len(coord),512,512],dtype=np.bool)\n",
    "        for j,cord in enumerate(coord):\n",
    "            segmented_mask_fill=segment_lung_mask(patient_pix,True,False)\n",
    "            if radius.iloc[j]>5:\n",
    "                #slice nodulecenter-1\n",
    "                noduleimages[index]=processimage(patient_pix[cord[0]-1])\n",
    "                nodulemasks[index]=cmw.cell_magic_wand(-patient_pix[int(cord[0])-1],[int(cord[2]),int(cord[1])],2,int(radius.iloc[j])+2)\n",
    "                rr,cc=circle(int(cord[2]),int(cord[1]),int(radius.iloc[j]))\n",
    "                imgcircle = np.zeros((512, 512), dtype=np.int16)\n",
    "                imgcircle[rr,cc]=1\n",
    "                nodulepixcircle=imgcircle*patient_pix[cord[0]-1]\n",
    "                nodulepixcircle[nodulepixcircle<thresh]=0\n",
    "                nodulepixcircle[nodulepixcircle!=0]=1\n",
    "                nodulemaskscircle[index]=nodulepixcircle.astype(np.bool)\n",
    "                \n",
    "                nodulepix=nodulemasks[index]*patient_pix[cord[0]-1]\n",
    "                nodulepix[nodulepix<thresh]=0\n",
    "                nodulepix[nodulepix!=0]=1\n",
    "                nodulemasks[index]=nodulepix.astype(np.bool)\n",
    "                index+=1\n",
    "                \n",
    "                #slice nodulecenter\n",
    "                noduleimages[index]=processimage(patient_pix[cord[0]])\n",
    "                nodulemasks[index]=cmw.cell_magic_wand(-patient_pix[int(cord[0])],[int(cord[2]),int(cord[1])],2,int(radius.iloc[j])+2)\n",
    "                nodulepix=nodulemasks[index]*patient_pix[cord[0]]\n",
    "                nodulepix[nodulepix<thresh]=0\n",
    "                nodulepixcircle=imgcircle*patient_pix[cord[0]]\n",
    "                nodulepixcircle[nodulepixcircle<thresh]=0\n",
    "                \n",
    "                #get mean nodule HU value\n",
    "\n",
    "                #get mean non-nodule HU value\n",
    "                nonnodule=(nodulemasks[index].astype(np.int16)-1)*-1*segmented_mask_fill[cord[0]]*patient_pix[cord[0]]\n",
    "                nonnodule[nonnodule<thresh]=0\n",
    "                nonnodulemeanhu.append(np.mean(nonnodule[nonnodule!=0]))\n",
    "                plt.figure()\n",
    "                #plt.hist(nodulepix[nodulepix!=0].flatten(),bins=80, alpha=0.5, color='blue')\n",
    "                plt.hist(nonnodule[nonnodule!=0].flatten(),bins=80, alpha=0.5, color='orange')\n",
    "                plt.hist(nodulepixcircle[nodulepix!=0].flatten(),bins=80,alpha=0.5, color='green')\n",
    "                plt.savefig(\"histplots/\"+meta['Patient Id'].loc[i]+\"slice\"+str(cord)+\".png\",dpi=300)\n",
    "                plt.close()\n",
    "                nodulemeanhu.append(np.mean(nodulepix[nodulepix!=0]))\n",
    "                nodulepix[nodulepix!=0]=1\n",
    "                nodulemasks[index]=nodulepix.astype(np.bool)\n",
    "                nodulepixcircle[nodulepixcircle!=0]=1\n",
    "                nodulemaskscircle[index]=nodulepixcircle.astype(np.bool)\n",
    "                index+=1\n",
    "                \n",
    "                #slice nodulecenter+1\n",
    "                noduleimages[index]=processimage(patient_pix[cord[0]+1])\n",
    "                nodulemasks[index]=cmw.cell_magic_wand(-patient_pix[int(cord[0])+1],[int(cord[2]),int(cord[1])],2,int(radius.iloc[j])+2)\n",
    "                nodulepix=nodulemasks[index]*patient_pix[cord[0]+1]\n",
    "                nodulepix[nodulepix<thresh]=0\n",
    "                nodulepix[nodulepix!=0]=1\n",
    "                nodulemasks[index]=nodulepix.astype(np.bool)\n",
    "                nodulepixcircle=imgcircle*patient_pix[cord[0]+1]\n",
    "                nodulepixcircle[nodulepixcircle<thresh]=0\n",
    "                nodulepixcircle[nodulepixcircle!=0]=1\n",
    "                nodulemaskscircle[index]=nodulepixcircle.astype(np.bool)\n",
    "                index+=1\n",
    "    elapsed_time=time.time()-start_time\n",
    "    totaltime=elapsed_time/(i+1)*len(patients)\n",
    "np.save(datafolder+'/noduleimages.npy',noduleimages)\n",
    "np.save(datafolder+'/nodulemasks.npy',nodulemasks)\n",
    "np.save(datafolder+'/nodulemaskscircle.npy',nodulemaskscircle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HVfUDYB+wdZFrkHSVGd/51VGXcM1a7NNBa4ATfcsngb+5yDVc2zwto2vQ0y//J/DJUVex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\n1DBDQJIaZghIUsP+P1nJKJiY7hQYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd48ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exploratory analysis\n",
    "plt.hist(nodulemeanhu, bins=20)\n",
    "plt.hist(nonnodulemeanhu)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab0b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab0bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae8b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XVA8xSZJa2YOQdmJnfwXP9ZdlX27EXMgc4/w3L+Rf6o7KuzDsQUiSWtmDkHroqfwFPN+/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2a7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nodulelocations['eq. diam.'], bins=80)\n",
    "plt.xlabel(\"Nodule Diameter (mm)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "\n",
    "plt.hist(nodulelocations['volume'].loc[nodulelocations['volume']<6000], bins=80)\n",
    "plt.xlabel(\"volume (cc)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "plt.hist(nodulelocations['volume'].loc[nodulelocations['volume']<1000], bins=80)\n",
    "plt.xlabel(\"volume (cc)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "plt.hist(nodulelocations['volume'].loc[nodulelocations['volume']<400], bins=80)\n",
    "plt.xlabel(\"volume (cc)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae606a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nodcount=[]\n",
    "for i in range(len(patients)):\n",
    "    coord=nodule_coordinates(nodulelocations,meta.iloc[i])\n",
    "    nodcount.append(len(coord))\n",
    "    \n",
    "plt.hist(nodcount, bins=25)\n",
    "plt.xlabel(\"Number of Nodules per Patient\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
